Systems and methods for electronically matching online user profiles

ABSTRACT

A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/804,661, filed Feb. 12, 2019, entitled“SYSTEMS AND METHODS FOR ELECTRONICALLY MATCHING ONLINE USER PROFILES,”and to U.S. Provisional Patent Application No. 62/835,272, filed Apr.17, 2019, entitled “SYSTEMS AND METHODS FOR ELECTRONICALLY MATCHINGONLINE USER PROFILES,” the entire contents and disclosures of which arehereby incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to electronically matchingonline user profiles and, more particularly, to network-based systemsand methods for leveraging social media data and insurance data tocreate and match online user profiles.

BACKGROUND

At least some known computer networks have enabled users to increasinglyinteract with each other. With the advent of technology-based socialinteraction, the sharing of resources and activities within localcommunities has increased dramatically. However, due to the lack of inperson interaction between users, many users are unwilling to trustother users, specifically when performing peer-to-peer buying/selling,renting, and/or sharing products and services using these known computernetworks.

Therefore, there is a need for networking architecture that may be usedto provide reliable, transparent, and secure information about users toother users.

BRIEF SUMMARY

The present embodiments may relate to systems and methods forelectronically generating, matching, and providing online user profiles,and determining a sharing score between the online user profiles. Thesystem may include a matching computing device, one or more insuranceprovider servers, one or more client systems, one or more social mediaservers, one or more third party servers, and/or one or more databases.

In one aspect, a matching computer system for determining a sharingscore between online user profiles is provided. The matching computersystem may include a processor in communication with at least one memorydevice. The processor may be configured to: (i) generate a first onlineuser profile associated with a first user, wherein the first online userprofile comprises at least one of first insurance data, first socialmedia data, first third party data, first user data, and first sharingdata associated with a first user; (ii) generate a second online userprofile associated with a second user, wherein the second online userprofile comprises at least one of second insurance data, second socialmedia data, second third party data, second user data, and secondsharing data associated with a second user; (iii) calculate a first basescore associated with the first user based upon the first online userprofile, wherein the first base score represents a first level oftrustworthiness of the first user; (iv) calculate a second base scoreassociated with the second user based upon the second online userprofile wherein the second base score represents a second level oftrustworthiness of the second user; and/or (v) determine a sharing scorebetween the first and second users based upon the first and second basescores, wherein the sharing score represents a level of matching betweenthe first online user profile and the second online user profile. Thecomputer system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a computer-implemented method for determining asharing score between online user profiles is provided. The method maybe implemented by a matching computer system including at least oneprocessor. The method may include: (i) generating a first online userprofile associated with a first user, wherein the first online userprofile comprises at least one of first insurance data, first socialmedia data, first third party data, first user data, and first sharingdata associated with a first user; (ii) generating a second online userprofile associated with a second user, wherein the second online userprofile comprises at least one of second insurance data, second socialmedia data, second third party data, second user data, and secondsharing data associated with a second user; (iii) calculating a firstbase score associated with the first user based upon the first onlineuser profile, wherein the first base score represents a first level oftrustworthiness of the first user; (iv) calculating a second base scoreassociated with the second user based upon the second online userprofile wherein the second base score represents a second level oftrustworthiness of the second user; and/or (v) determining a sharingscore between the first and second users based upon the first and secondbase scores, wherein the sharing score represents a level of matchingbetween the first online user profile and the second online userprofile. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

In yet another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor determining a sharing score between online user profiles isprovided. When executed by at least one processor, thecomputer-executable instructions cause the processor to: (i) generate afirst online user profile associated with a first user, wherein thefirst online user profile comprises at least one of first insurancedata, first social media data, first third party data, first user data,and first sharing data associated with a first user; (ii) generate asecond online user profile associated with a second user, wherein thesecond online user profile comprises at least one of second insurancedata, second social media data, second third party data, second userdata, and second sharing data associated with a second user; (iii)calculate a first base score associated with the first user based uponthe first online user profile, wherein the first base score represents afirst level of trustworthiness of the first user; (iv) calculate asecond base score associated with the second user based upon the secondonline user profile wherein the second base score represents a secondlevel of trustworthiness of the second user; and/or (v) determine asharing score between the first and second users based upon the firstand second base scores, wherein the sharing score represents a level ofmatching between the first online user profile and the second onlineuser profile. The instructions may direct additional, less, or alternatefunctionality, including that discussed elsewhere herein.

In another aspect, a matching computer system for determining a sharingscore between online user profiles is provided. The matching computersystem may include a processor in communication with at least one memorydevice. The processor may be configured to: (i) receive a user-listingassociated with a first user, wherein the user-listing includes aplurality of items offered for at least sale, rent, and lease, andwherein the first user is associated with a first user-typecorresponding to an owner; (ii) calculate an item base score for each ofthe plurality of items, each item base score calculated based upon afirst online user profile associated with the first user; (iii)calculate a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and/or(iv) determine a sharing score between each of the plurality of itemsand the second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile. The computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

In yet another aspect, a computer-implemented method for determining asharing score between online user profiles is provided. The method maybe implemented by a matching computer system including at least oneprocessor. The method may include: (i) receiving a user-listingassociated with a first user, wherein the user-listing includes aplurality of items offered for at least sale, rent, and lease, andwherein the first user is associated with a first user-typecorresponding to an owner; (ii) calculating an item base score for eachof the plurality of items, each item base score calculated based upon afirst online user profile associated with the first user; (iii)calculating a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and/or(iv) determining a sharing score between each of the plurality of itemsand the second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile. The method may include additional, less,or alternate actions, including those discussed elsewhere herein.

In another aspect, at least one non-transitory computer-readable storagemedia having computer-executable instructions embodied thereon fordetermining a sharing score between online user profiles is provided.When executed by at least one processor, the computer-executableinstructions cause the processor to: (i) receive a user-listingassociated with a first user, wherein the user-listing includes aplurality of items offered for at least sale, rent, and lease, andwherein the first user is associated with a first user-typecorresponding to an owner; (ii) calculate an item base score for each ofthe plurality of items, each item base score calculated based upon afirst online user profile associated with the first user; (iii)calculate a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and/or(iv) determine a sharing score between each of the plurality of itemsand the second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile. The instructions may direct additional,less, or alternate functionality, including that discussed elsewhereherein.

In yet another aspect, a matching computer system for determining atrust score for a user based upon at least social media data andinsurance data is provided. The matching computer system may include aprocessor in communication with at least one memory device. Theprocessor may be configured to: (i) register, with the matching computersystem, one or more users; (ii) receive consent from the one or moreusers to capture social media data associated with social mediaactivities of each respective user; (iii) collect the social media dataand insurance data from each registered user; (iv) retrieve the socialmedia data and the insurance data associated with each registered user;and/or (v) determine a trust score for each registered user based, atleast in part, upon each respective social media data and eachrespective insurance data, wherein the trust score represents a level oftrustworthiness of the user. The computer system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

In another aspect, a computer-implemented method for determining a trustscore for a user based upon at least social media data and insurancedata is provided. The method may be implemented by a matching computersystem including at least one processor. The method may include: (i)registering, with the matching computer system, one or more users; (ii)receiving consent from the one or more users to capture social mediadata associated with social media activities of each respective user;(iii) collecting the social media data and insurance data from eachregistered user; (iv) retrieving the social media data and the insurancedata associated with each registered user; and/or (v) determining atrust score for each registered user based, at least in part, upon eachrespective social media data and each respective insurance data, whereinthe trust score represents a level of trustworthiness of the user. Themethod may include additional, less, or alternate actions, includingthose discussed elsewhere herein.

In yet another aspect, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor determining a trust score for a user based upon at least socialmedia data and insurance data is provided. When executed by at least oneprocessor, the computer-executable instructions cause the processor to:(i) register, with the matching computer system, one or more users; (ii)receive consent from the one or more users to capture social media dataassociated with social media activities of each respective user; (iii)collect the social media data and insurance data from each registereduser; (iv) retrieve the social media data and the insurance dataassociated with each registered user; and/or (v) determine a trust scorefor each registered user based, at least in part, upon each respectivesocial media data and each respective insurance data, wherein the trustscore represents a level of trustworthiness of the user. Theinstructions may direct additional, less, or alternate functionality,including that discussed elsewhere herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the systems andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed systemsand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and are instrumentalitiesshown, wherein:

FIG. 1 illustrates an exemplary matching computer system forelectronically matching profiles in accordance with the presentdisclosure;

FIG. 2 illustrates is an exemplary configuration of a database used bythe matching computer system shown in FIG. 1 for generating online userprofiles;

FIG. 3 illustrates an exemplary configuration of an exemplary usercomputing device that may be used in the matching computer systemillustrated in FIG. 1 ;

FIG. 4 illustrates an exemplary configuration of an exemplary servercomputing device that may be used in the matching computer systemillustrated in FIG. 1 ;

FIG. 5 illustrates an exemplary graphical user interface of a computerapplication implemented by the matching computer system shown in FIG. 1.

FIG. 6 illustrates an exemplary graphical user interface of a computerapplication implemented by the matching computer system shown in FIG. 1.

FIG. 7 illustrates an exemplary graphical user interface of a computerapplication implemented by the matching computer system shown in FIG. 1.

FIG. 8 illustrates an exemplary graphical user interface of a computerapplication implemented by the matching computer system shown in FIG. 1.

FIG. 9 illustrates a flow chart of an exemplary computer-implementedmethod implemented by the exemplary matching computer system shown inFIG. 1 ; and

FIG. 10 illustrates a diagram of components of one or more exemplarycomputing devices that may be used in the matching computer system shownin FIG. 1 .

FIG. 11 illustrates an exemplary computer-implemented method ofconducting a P2P rental vehicle transaction.

FIG. 12 illustrates an exemplary computer-implemented method ofconducting a P2P rental home transaction.

FIG. 13 illustrates an exemplary diagram of a sharing score calculationimplemented by the matching computer system shown in FIG. 1 .

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

By partnering with a social media platform, such as Facebook®, aninsurance provider, such as State Farm®, may be able to use data metricscollected by social media, as well as insurance data, to create a “trustscore” (also referred to herein as “base score”) that would rate usersbased upon several criteria. This criteria would include, but would notbe limited to, insurance claims, credit score, telematics data, paymenthistory, tenure, as well the social media activities, such as how longthe users have been on the platform, number of posts, the degree towhich the dependent people are connected and history on the marketplace.The trust score represents a level of reliability (e.g.,trustworthiness) of a user associated with an online user profile, asdiscussed elsewhere herein.

Customers are unwilling to trust strangers when peer-to-peer (P2P)buying/selling or sharing products and services online. Furthermorecustomers are wanting a way to vet others to know the transaction issafe. Also, it is believed that currently there are no services bringingtogether multiple data points to create a trust score for P2Ptransactions.

With the present embodiments, an entity or computer system will leveragethe ability to quickly and efficiently gather data with a user'spermission or affirmative consent (e.g., auto, home, and insurance data,as well as credit history and telematics data) to create one half oftrusting score system for a P2P transaction. This system will becombined with key metrics of a social media platform (tenure,connections, engagement, etc.) to complete a trustworthy scoring system.

The scoring computer system will be used in a P2P sharing platform whichcould actually be launched on one of various social media platforms.Furthermore, this platform or system may be configured to provide afeedback loop which will increase the accuracy of the scoring system. Byutilizing a customer rating system for every transaction, the computersystem will be able to automatically update and refine the weighting andscoring algorithm.

With the present embodiments, using data from insurance increasessecurity and trust-worthiness of the social media platform while thesocial media platform will help increases user engagement. The systemwould “live” on the social media platform, but would include theentity's, such as an insurance provider's, scoring data (for instance,an entity may not be sharing credit information or other data, ratherjust a composite score)—and the insurance provider (or other entity) mayalso provide the insurance on the P2P platform. For instance, for everyP2P transaction, such as home rental or vehicle rental, UBI (usage-basedinsurance) may be offered and/or quote based upon one or more scores,composite scores, trustworthiness scores, and/or home, vehicle, or userrisk profiles.

An exemplary scoring system is outlined below. Exemplary scoring metricsfor an insurance provider and social media may include the following.

State Farm Social Media Payment Credit Claims Connection Market Engage-Similar Telematics History Report History Tenure of friends Tenure PlaceHistory ment social circles Weight .15 .05 .1 .2 .05 .1 .05 .15 .05 .05Score 8 10 7 8 3 6 9 4 10 8 Weighted 1.2 .5 .7 1.6 .15 .6 .45 .6 .5 .4Score

This example would give a total weighted score or trust score of 6.7 outof 10. This weighted score would change as history on social mediaand/or insurance data changes.

Exemplary Sharing Score Overview

The present embodiments may include a computer system that generates orcalculates a “Sharing Score.” The “Sharing Score” is a number that iscomputed when there is a potential car sharing transaction between twopeople. This number is mathematically derived using a combination ofdata provided by an insurance provider and a social media platform, suchas telematics and other insurance-related data, and social mediaactivity-related data.

Each individual person may have their own “Base Score” computed from acombination of insurance data and social media as shown in the tablebelow (exemplary insurance factors displayed in columns 2-6, andexemplary social media factors in columns 6-8). Different criteria isused based on the role of the individual as either an owner or renter ofa vehicle. FB is short for Facebook®.

The Base Scores of an individual renter and individual owner aremathematically combined to create a unique Sharing Score. Additionaldata may be used to create the Sharing Score, which may include daterelated to, but not limited to, mutual friends and similar interests onsocial media, such as Facebook®.

Owner Rating Background Insurance Current Maintenance # of FB ActivityLength of Check Tagged Titled Friends on FB time on FB Renter RatingBackground Driver’s Driving Telematics # of FB Activity Length of CheckLicense Record Friends on FB time on FB

FIG. 13 illustrates a diagram of a sharing score calculation. Diagram1300 shows that Renter #1 has a base sharing score of 8.5 (shown inblock 1314) and is not as connected to Owner #1, who has a base sharingscore of 9.5 (shown in block 1304), as compared to Owner #2, who alsohas a base sharing score of 9.5 (shown in block 1306). For example,Renter #1 and Owner #2 may be more connected because both of them likethe same team 1308, whereas Renter #1 and Owner #1 may not be asconnected because they like different teams (e.g., Renter #1 likes team1308 and Owner #1 likes team 1302). Because of this lower connectionlevel, the overall Sharing Score dropped to 7.5 between Renter #1 andOwner #1, as shown in block 1310. Since Renter #1 is more connected toOwner #2, the overall Sharing Score increases to 9.75, as shown in block1312. In this example, Renter #1 should feel more confident renting fromOwner #2 and vice-versa.

Exemplary Matching

The systems and methods described herein relate to, inter alia, systemsand methods for generating, matching, and providing online user profilesbased upon social media data, insurance data, third party data, and userdata. In at least one embodiment, a matching computer system may includea matching computing device, an insurance provider server, a socialmedia server, a third party server, a client device, and a database. Inother embodiments, the matching computer system may include a pluralityof matching computing devices, insurance provider servers, social mediaservers, third party servers, client devices, and databases.

In the exemplary embodiment, the methods may be performed by thematching computing device. The matching computing device may be incommunication with the insurance provider server, the social mediaserver, the third party server, the client device, and the database. Thematching computing device may be configured to receive and/or transmitinsurance data from/to the insurance provider server, social media datafrom/to the social media server, third party data from/to the thirdparty server, user data and/or sharing data from/to the client device,and/or user profile data from/to the database. The matching computersystem may use the data described above to build, store, and updateonline user profiles, generate a base score for each online userprofile, compare online user profiles, and generate “sharing scores”based upon the comparison.

In the exemplary embodiment, the matching computing device is configuredto build online user profiles (e.g., owner profile or renter profile)corresponding to different types of users or user-types (e.g., owner,lessor, renter, or lessee). For example, a user may be an owner orlessor of a vehicle seeking to rent or lease his/her own vehicle toanother user. The matching computing device may be configured to buildan owner profile for the owner or lessor. In another example, a user maybe seeking to rent or lease a vehicle from another user. The matchingcomputing device may be configured to build a renter profile for theuser seeking to rent or lease (e.g., renter or lessee). In the exemplaryembodiment, the matching computing device may generate or calculate abase score for that particular user based upon the type of user, theinsurance data, the social media data, the third party data, the userdata, and/or the user profile data.

The base score represents, for example, a level of reliability (e.g.,trustworthiness) of a user associated with an online user profile. Forexample, a high base score for an owner of a vehicle may represent thatthe owner has not had accidents with the vehicle, has kept maintenanceof the vehicle on schedule, does not have convictions on his/herbackground check, has high activity on social media (e.g., dailyinteraction on social media), and/or has been on social media for a longperiod of time (e.g., more than a predefined period of time). Incontrast, a low score for an owner of a vehicle may represent that theowner has had accident with the vehicle, has not kept maintenance of thevehicle on schedule, convictions on his/her background check, has lowactivity on social media (e.g., once a week interaction on socialmedia), and/or has been on social media for a short period of time(e.g., less than a predefined period of time).

In another example, a high score for a renter of a vehicle may representthat the renter has no traffic tickets on his/her driving record, doesnot have convictions on his/her background check, is a conservativedriver based upon his/her vehicle telematics, has high activity onsocial media (e.g., daily interaction on social media), and/or has beenon social media for a long period of time (e.g., more than a predefinedperiod of time). In contrast, a low score for a renter of a vehicle mayrepresent that the renter has traffic tickets on his/her driving record,has convictions on his/her background check, is an aggressive driverbased upon his/her vehicle telematics, has low activity on social media(e.g., once a week interaction on social media), and/or has been onsocial media for a short period of time (e.g., less than a predefinedperiod of time).

By building, storing, and updating the online user profiles, thematching computing device avoids re-computing from scratch each onlineuser profile. Thus, by building and pre-storing the online userprofiles, the matching computer system improves bandwidth usage andprocessing speeds.

In the exemplary embodiment, the matching computing device receives userdata from one or more client devices associated with the users. Thematching computing device may use the user data to register the userswithin the matching computer system. For example, a user may download acomputer application to her client device (e.g., a user computingdevice) and input user data into the computer application forregistration with the matching computer system. The user may also accessa website of the matching computer system using a web browser, and inputuser data into the website to register with the matching computersystem.

The matching computing device may also use the user data to generate andupdate online user profiles. In the example embodiment, the user datamay include vehicle telematics, a user identifier, a user name, a userpassword, a client device IP address, user email address, a userlocation (e.g., user home address, user work address, or any otherlocation the user may input), user phone number, user financial accountinformation, user vehicle information (e.g., vehicle make, model, type,year, license plate number, picture(s), feature(s), mileage, rentalprice, or the like) and/or other data associated with a vehicle of theuser and/or the user.

In the exemplary embodiment, user data may include a user-type, whichmay be used to categorize online user profiles. In one embodiment, auser may be a provider of goods and/or services. For example, theprovider may be an owner of a vehicle seeking to rent his/her ownvehicle to another user, and this user's user-type may be “owner” or“lessor.” In another embodiment, a user may be a consumer. For example,the consumer may be seeking to rent a vehicle from another user, andthis user's user-type might be “renter” or “lessee.” In otherembodiments, the users may be users seeking to share (for value or not)other types of goods or services, such as real estate (e.g., apartments,houses, or the like), venues (e.g., pavilions, conference rooms,restaurants, sports venues, or the like), catering services, and othergoods or services that may be shared between users. The user-type of auser may be manually selected by the user or it may be automaticallyassigned by the matching computing device (e.g., a default type of userwhen none is selected by the user). Additionally, a user may selectmultiple user-types, such as “renter” and “owner.”

In the exemplary embodiment, user data may also include user-listings,which may include items, such as good(s) and/or service(s) a user wantsto display, offer for sale, offer for rent, and/or offer to lease toother users. In the exemplary embodiment, user-listings are associatedwith owner profiles and may include vehicles owned by the user that theuser wishes to rent out to other users. Multiple user-listings may beincluded in any given owner profile. For example, an owner may listmultiple vehicles for rent, which the matching computing device maydisplay in the form of a list on a client device.

In the exemplary embodiment, the matching computing device may alsoreceive social media data from one or more social media serversassociated with social media (e.g., FACEBOOK®, INSTAGRAM®, SNAPCHAT®,GOOGLE™ or other type of social media). The matching computing devicemay use the social media data to register users with the matchingcomputer system. For example, a user may download a computer applicationto her client device (e.g., a user computing device) and access a socialmedia site for registration with the matching computer system. The usermay also access a website of the matching computer system using a webbrowser, and access a social media site from the website to registerwith the matching computer system. The matching computing device mayalso use the social media data to generate and update online userprofiles. The social media data may include a social media identifier, asocial media name (e.g., FACEBOOK®, INSTAGRAM®, SNAPCHAT®, or othersocial network names), a user identifier, a user name, a number offollowers and/or friends associated with each social media, liked pages,marketplace data, and/or other social media data associated with theuser.

In the exemplary embodiment, the matching computing device may receivethird party data from one or more third party servers. The matchingcomputing device may use the third party data to update online userprofiles. For example, the matching computing device may receive creditscore information from a third party server associated with a creditbureau agency (e.g., Experian®, TransUnion®, Equifax®) and update onlineuser profiles accordingly. The third party data may include credit data(e.g., credit score, credit bureau identifier, user identifier, credithistory, and/or other data associated with the credit information),public record data (e.g., public records available to the public, suchas public offenses committed by users, an individual identifier, a typeof offense, a time and date of offense, a place of offense, and/or otherdata associated with public records), vehicle history reports (e.g.,CARFAX™, AUTOCHECK® by Experian, or the like) and/or other data that athird party may provide to the matching computing device.

In the exemplary embodiment, the matching computing device may receiveinsurance data from one or more insurance provider servers. The matchingcomputing device may use the insurance data to update online userprofiles. For example, the matching computing device may receive aninsurance claim from an insurance provider server associated with aninsurance provider (e.g., State Farm) and update online user profilesaccordingly. The insurance data may include similar data points to thethird party data, such as public record data and credit data, and theuser data, such as vehicle telematics. The insurance data may alsoinclude insurance provider identifier, insurance type, insurancecoverage, insurance tenure, insurance payment history, a number ofinsurance claims, insurance type of claims, insurance driving rating,background check, driver license number, driving record, telematicsdata, maintenance records, current tagged title, and/or other data thatan insurance provider may provide to the matching computing device.

In the exemplary embodiment, the matching computing device may receivesharing data from client devices. The sharing data may include data suchas peer experience ratings, experience comments, length of rental,and/or other sharing data that users may input regarding theirexperience while sharing a good and/or service. The matching computingdevice may also compute historical sharing data, such as a number oftimes an user has requested to share and/or has shared a good and/orservice, an overall peer experience rating (e.g., computing a ratingbased upon peer experience received), number of times a user hasaccepted and/or declined to share, and/or other historical sharing datathat the matching computing device may compute based upon dataassociated with user sharing activity.

In the exemplary embodiment, the matching computing device may storeinsurance data, social media data, third party data, user data, and/orsharing data in a database. The matching computing device may use any ofthe stored data to generate online user profile data for each onlineuser profile. The matching computing device may also use the online userprofiles, and particularly, the user profile data to generate a basescore for each online user profile, compare the online user profiles toother online user profiles, and generate sharing scores based upon thecomparison.

The sharing score represents an overall matching of online userprofiles. More specifically, a high sharing score represents usershaving online user profiles that more closely match one another, andthus indicates a high likelihood that two users would be a good fit forsharing goods and/or services. In contrast, a low sharing scorerepresents users having online user profiles that do not closely matchone another, and thus indicates a high likelihood that two users wouldnot be a good fit for sharing goods and/or services.

Concurrent to or after generating the online user profile, the matchingcomputing device is configured to compute a base score for each onlineuser profile, and store the base score with its corresponding onlineuser profile. In the exemplary embodiment, the matching computing deviceuses insurance data and social media data to compute base scores. Inother embodiments, the matching computing device may use other data,such as user data, sharing data, user profile data, and/or othersuitable data that enables matching computing device to compute basescores.

Based upon the type of user associated with each online user profile,the matching computing device is configured to determine the type ofdata that may be used to generate each base score. For example, thematching computing device may generate a base score for an owner usinginsurance data, such as insurance type, current tagged titled, andvehicle maintenance, whereas for generating a base score for a renter,the matching computing device may use other insurance data, such asdriver's license number, driving record, and vehicle telematics.

In an alternative embodiment, the matching computing device may generatea base score for each item listed in a user-listing of an owner basedupon data associated with the owner profile and the particularuser-listing. For example, the matching computing device may useinsurance data and social media data to determine a base score for anowner and further use data about a particular vehicle listed by theowner to determine a base score for the vehicle.

In the exemplary embodiment, the matching computing device is configuredto generate a sharing score between two users based upon data containedin the online user profile associated with each user. In one embodiment,the matching computing device calculates a sharing score between twousers based upon a base score for a first user and a base score for asecond user. For example, the matching computing device may compute asharing score for a renter and an owner based upon the base scores forthe two users. The higher the sharing score, the better the matchbetween the renter and the owner. In contrast, the lower the sharingscore, the poorer the match between the two users.

The sharing score is a number representing the likelihood that two userswould be a good fit for sharing goods and/or services. This number ismathematically derived using a combination of data associated with thetwo users, such as insurance data provided by an insurance providerserver, social media data provided by a social media server, third partydata provided by a third party server, user data and/or sharing dataprovided by a client device, a base score for each user, and/or anyother data retrieved from a database. The matching computing device maybe configured to store the sharing scores in a database.

For example, a user A, a user B, and a user C may register with thematching computer system each using a client device. After registeringwith the matching computer system, the matching computing device isconfigured to generate an online user profile for each user. For sake ofsimplicity, in this example, users A and B each select to be an owner ofa vehicle (e.g., user-type: owner), and user C selects to be a renter ofa vehicle (e.g., user-type: renter). In other examples, user A, user B,and/or user C may select to be an owner, a renter, or both, of a vehicleor of other types of goods and/or services. After the type of user hasbeen selected, the matching computing device is configured to compute abase score for each user. In this example, the matching computing devicecomputes a base score of 9.5 for user A, a base score of 9.5 for user B,and a base score of 8.5 for user C. In this example, the base score isbased upon a scale 0 to 10. This scale can be different in differentembodiments.

The matching computing device may parse one or more social media serversand retrieve social media data, such as liked pages and/or followingentities (e.g., people and/or institutions) by the users. In thisexample, the matching computing device retrieves, from one or moresocial media servers, information related to liked pages and/orfollowing entities that are associated with baseball. By retrieving thisinformation, the matching computing device is able to determine thebaseball team that each user likes. The matching computing device mayuse the liked baseball teams as a factor to compute a sharing scorebetween two users.

In this example, the matching computing device determines that user Alikes Team 1 and users B and C like Team 2. Team 1 being a differentteam than Team 2. Team 1 being a rival of Team 2. After making thedetermination, the matching computing device may add or include theliked teams to each online user profile. The matching computing devicemay then compute a first sharing score between users A (owner) and C(renter), and a second sharing score between users B (owner) and C(renter). As stated above, users A and B each had the same base score of9.5. However, when the matching computing device computes the sharingscores, the matching computing device may determine that the firstsharing score (the sharing score between users A and C) may be lowerthan the second sharing score (the sharing score between users B and C).In this example, the difference among the first and the second sharingscores is due to the different baseballs teams that the users like.Because users A and C like different baseball teams and users B and Clike the same baseball team, the sharing score of users A and C is lowerthan the score of users B and C.

In other examples, a sharing score may be higher or lower than othersdue to other elements (e.g., activities, hobbies, sports, careers,education, work place, place of residence, or the like) associated withonline user profiles. In other words, the matching computing device maybe configured to factor in the computation of sharing scores thecompatibility of a variety of elements associated with the online userprofiles.

In an alternative embodiment, the matching computing device may beconfigured to generate a sharing score between a user (e.g., an owner)and each item included in a user-listing associated with another user(e.g., a renter). In some embodiments, the matching computing device maybe configured to generate a base score for each user (e.g., user basescore) and/or a base score for each item in a user-listing (e.g., anitem base score). In one embodiment, the matching computing device maybe configured to generate a sharing score between the first user andeach item included in the user-listing based upon the user base scoreand each item base score.

In another embodiment, the matching computing device may be configuredto generate a sharing score between the first user and each itemincluded in the user-listing based upon base scores and/or other data,such as insurance data, social media data, third party data, user data,or any other relevant data. For example, a first user may be looking torent a vehicle, and a second user may have 3 separate vehicles listedfor rent. The matching computing device may generate a sharing scorebetween the first user and each of the 3 vehicles listed for rent by thesecond user based upon criteria set by the first user, base scores,and/or any other data associated with the two users. As a specificexample, the first user may be looking for a car with room for multiplepassengers, and the matching computing device may generate a lowersharing score for a 2-door sedan than for a mini-van, even though bothvehicles are listed by the same user.

In the exemplary embodiment, the matching computing device may receive,from a client device via a computer application or a website, a rentalrequest initiated by a renter (e.g., a registered user). The user mayinitiate the rental request by accessing the computer application or thewebsite and sign into the matching system. Once the renter has signedinto the matching system, the matching computing device is configured tocollect data from the client device. For example, the matching computingdevice may collect data, such as a geolocation (e.g., global positionsystem (GPS)) of the client device, a location input by the renter,duration of rental (e.g., pick up day(s) and time(s), drop off day(s)and time(s)), maximum and/or minimum distance from the geolocation ofthe client device or input location to search for availableuser-listings, maximum and/or minimum rental price, and/or other datathat the matching computing device may request to operate, as describedherein. The matching computing device may also be configured to sendinstructions to the client device to display filters (e.g., filter byvehicle make, vehicle type, distance, or the like) that may be input bythe user.

In the exemplary embodiment, the matching computing device may comparethe collected data to data stored in online user profiles, and generatesharing scores based upon the comparison, as described above. Thematching computing device may also instruct the client device to displaya list of one or more user-listings that match the collected data andthe online user profile of the renter (e.g., renter profile).

A user may click on one of the user-listings triggering the clientdevice to transmit a selection to the matching computing device. Forexample, a renter may initiate a rental request by inputting a filter,such as vehicle type (e.g., a pickup truck) into his client device. Theclient device transmits the rental request, including the filter, to thematching computing device. The matching computing device parses adatabase to perform a look up for online user profiles (e.g., ownerprofiles) including pickup trucks. Once the matching computing devicecollects the owner profiles, the matching computing device may generatea sharing score between each owner profile and the renter profile.

The matching computing device may also transmit to the client deviceinstructions to display a list of the pickup trucks associated with theowner profiles and the sharing scores between the renter profile andeach owner profile. In this example, the matching computing deviceinstructs the client device to display a list of pickup trucks that maybe selected by the renter. Once the renter clicks on one of the pickuptrucks listed, the matching computing device transmits instructions tothe client device to display, concurrently with the list, a dynamic mapshowing at least the location of the pickup truck and the sharing scorebetween the owner profile of the pickup truck and the renter profile. Insome embodiments, the sharing score is displayed as a range. In otherembodiments, the sharing score is displayed as an integer or a decimalnumber.

In this example, the renter may click on the map to expand a viewincluding details of the pickup truck (e.g., availability, location, orthe like) and the owner profile (ratings, length in social media, or thelike). Once the renter selects to rent a pickup truck, the client devicetransmits the selection to the matching computing device, whichtransmits, to a client device of the owner of the selected pickup truck,a request to rent the selected pickup truck. If the owner accepts therequest, the matching computing device forwards the request to therenter's client device, and payment processing is initiated via anelectronic marketplace associated with one or more social media servers,or using person-to-person payment process (e.g., from one account toanother account).

Peer-to-peer commerce systems (also known as sharing economy systems)allow for the exchange of goods and/or services on an individual basis,so that individuals are exchanging the goods and/or services with otherindividuals. Examples of these exchanges, include but are not limited toonline auctions, online classifieds, ride sharing, residence sharing,vehicle sharing, commute sharing, and travel sharing. In most sharingeconomy systems, a list of individuals that are willing to offer aservice, such as a vehicle ride, vehicle rental, or a residence rental,list their proposed transaction on a website or other online platform.In one embodiment, an individual interested in renting (e.g., a renter)a good or service may advertise a demand to rent the good or service tomultiple parties (e.g., multiple owners). Similarly, an individualinterested in renting his or her own good or service may advertise anoffer to rent the good or service to multiple parties (e.g., multiplerenters).

At least one of the technical problems addressed by this system mayinclude: (i) increasing the accessibility of stored data associated witha particular user; (ii) connecting users based upon a comprehensive setof data associated with each user; (iii) providing a particular,convenient aggregate of data useful to a user; (iv) providing a moreefficient electronic marketplace platform by matching users together;and (v) generating improved and more targeted transactions by reducinguser complaints, fraud, and bandwidth usage (e.g., less messagingtraffic across the system).

The methods and systems described herein may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware, or any combination or subset thereof,wherein the technical effects may be achieved by performing at least oneof the following steps: (i) generating a first online user profileassociated with a first user, wherein the first online user profilecomprises at least one of first insurance data, first social media data,first third party data, first user data, and first sharing dataassociated with a first user; (ii) generating a second online userprofile associated with a second user, wherein the second online userprofile comprises at least one of second insurance data, second socialmedia data, second third party data, second user data, and secondsharing data associated with a second user; (iii) calculating a firstbase score associated with the first user based upon the first onlineuser profile, wherein the first base score represents a first level oftrustworthiness of the first user; (iv) calculating a second base scoreassociated with the second user based upon the second online userprofile wherein the second base score represents a second level oftrustworthiness of the second user; and (v) determining a sharing scorebetween the first and second users based upon the first and second basescores, wherein the sharing score represents a level of matching betweenthe first online user profile and the second online user profile.

Additionally or alternatively, the technical effects may be achieved byperforming at least one of the following steps: (i) receiving auser-listing associated with a first user, wherein the user-listingincludes a plurality of items offered for at least sale, rent, andlease, and wherein the first user is associated with a first user-typecorresponding to an owner; (ii) calculating an item base score for eachof the plurality of items, each item base score calculated based upon afirst online user profile associated with the first user; (iii)calculating a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and (iv)determining a sharing score between each of the plurality of items andthe second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile.

Exemplary Matching System

FIG. 1 depicts a view of an exemplary matching system 100 that may beused to build and update online user profiles, generate a base score foreach online user profile, compare online user profiles, and generatesharing scores based upon the comparison. Matching system 100 mayinclude matching computing device 102, which may be in communicationwith insurance provider server 104. In some embodiments, matchingcomputing device 102 is a component of insurance provider server 104,while in others, matching computing device 102 is separate frominsurance provider server 104. In the exemplary embodiment, matchingcomputing device 102 is in further communication with client devices106, third party servers 108 and social media servers 110. Matchingcomputing device 102 is also in communication with database 112 and maycommunicate with database 112 through database server 114. In someembodiments, database server 114 is a component of insurance providerserver 104. In other embodiments, database server 114 is separate frominsurance provider server 104. In some embodiments, matching system 100may include a plurality of matching computing devices, client devices,third party servers, social media servers, and/or databases.

In the exemplary embodiment, matching computing device 102 is configuredto build, store, and update online user profiles. Matching computingdevice 102 may receive user data from client devices 106 and use theuser data to register users and generate and update online userprofiles. For example, a user may download a computer application to herclient device (e.g., a user computing device) and input user data intothe computer application for registration with the matching system. Theuser may also access a website of the matching system using a webbrowser, and input user data into the website to register with thematching system. The user data may include vehicle telematics, a useridentifier, a user name, a user password, a client device IP address,user email address, user home address, user phone number, user financialaccount information, user vehicle information (e.g., vehicle make,model, type, year, license plate number, picture(s), feature(s),mileage, rental price, or the like) and/or other data associated with avehicle of the user and/or the user.

User data may also include a user-type associated with the user, and maybe selected by the user or automatically assigned by matching computingdevice 102. For example, a user registering with matching system 100 forthe purpose of acquiring a rental car may select her user-type as“renter.” As another example, a user registering with matching system100 for the purpose of distributing a rental car may select heruser-type as “owner.” Additionally, user data may include user-listingswhich correspond to items (e.g., good(s) and/or service(s)) a user haslisted or registered with matching system 100. For example, a user whowould like to lease a car to another user may register her car as auser-listing. In some embodiments, multiple user-listings may beassociated with a single user.

Matching computing device 102 may also receive sharing data from clientdevices 106 and may use the sharing data to update online user profiles.The sharing data may include data such as peer experience rating,experience comments, length of rental, and/or other sharing data thatusers may input regarding their experience while sharing a good and/orservice through matching system 100. Matching computing device 102 mayalso compute historical sharing data, such as a number of times an userhas requested to share and/or has shared a good and/or service, anoverall peer experience rating (e.g., computing a rating based upon peerexperience received), number of times an user has accepted and/ordeclined to share, and/or other historical sharing data that matchingcomputing device 102 may compute based upon data associated with sharingexperiences.

Matching computing device 102 may receive social media data from socialmedia servers 110 and may use the social media data to register usersand generate and update online user profiles. For example, a user maydownload a computer application to her client device (e.g., a usercomputing device) and access a social media site for registration withthe matching system. The social media data may include a social mediaidentifier, a social media name (e.g., FACEBOOK®, INSTAGRAM®, SNAPCHAT®,or other social network names), a user identifier, a user name, a numberof followers and/or friends associated with each social media, likedpages, marketplace data, and/or other social media data associated withthe user.

Matching computing device 102 may receive third party data from thirdparty servers 108 and may use the third party data to generate andupdate online user profiles. For example, the matching computing devicemay receive credit score information from a third party serverassociated with a credit bureau agency (e.g., Experian®, TransUnion®,Equifax®) and update online user profiles accordingly. The third partydata may include credit data (e.g., credit score, credit bureauidentifier, user identifier, credit history, and/or other dataassociated with the credit information), public record data (e.g.,public records available to the public, such as public offensescommitted by users, an individual identifier, a type of offense, a timeand date of offense, a place of offense, and/or other data associatedwith public records), vehicle history reports (e.g., CARFAX™ AUTOCHECK®by Experian, or the like) and/or other data that a third party mayprovide to the matching computing device.

Matching computing device 102 may further receive insurance data frominsurance provider server 104 and may use the insurance data to generateand update online user profiles. For example, the matching computingdevice may receive an insurance claim from an insurance provider serverassociated with an insurance provider (e.g., State Farm®) and updateonline user profiles accordingly. The insurance data may include similardata points to the third party data, such as public record data andcredit data, and the user data, such as vehicle telematics. Theinsurance data may also include insurance provider identifier, insurancetype, insurance coverage, insurance tenure, insurance payment history, anumber of insurance claims, insurance type of claims, insurance drivingrating, background check, driver license number, driving record,telematics data, maintenance records, current tagged title, and/or otherdata that an insurance provider may provide to the matching computingdevice.

In the exemplary embodiment, matching computing device 102 uses theonline user profiles to generate a base score for each online userprofile, compare the online user profiles, and generate sharing scoresbased upon the comparison. Matching computing device 102 may beconfigured to compute a base score for each online user profile andstore the base score with its corresponding online user profile. Forexample, matching computing device 102 may use any of user data, usertype, social media data, insurance data, third party data, and/orsharing data to determine a base score. In some embodiments, matchingcomputing device 102 determines the criteria for calculating a basescore based upon any of the types of data described above. In otherwords, the equation used for calculating a user's base score may changebased upon certain information included in that user's online userprofile. For example, the base score for a user with user-type “renter”may be calculated using different data than the base score for a userwith user-type “owner.”

Matching computing device 102 may be configured to generate sharingscores between online user profiles based upon comparing online userprofiles. Specifically, matching computing device 102 may compare datasuch as user data, insurance data, social media data, and sharing datacontained in two different online user profiles and generate a sharingscore between the two online user profiles based upon the comparison ofthe data. For example, matching computing device 102 may generate asharing score between online user profile A and online user profile B bycomparing insurance data, user preferences, and social media datacontained in the two online user profiles. In one embodiment, matchingcomputing device 102 includes base scores in the generation of a sharingscore. Specifically, when an online user profile is created and updated,matching computing device 102 may generate a base score for the onlineuser profile. Matching computing device 102 then compares at least twoonline user profiles, including the base scores and any of the datatypes listed above, and generates a sharing score between the twoprofiles. For example, matching computing device 102 may determine afirst base score for a first online user profile and a second base scorefor a second online user profile. Matching computing device 102 may thencompare the first online user profile and second online user profile bycorrelating social media data, insurance data, and base scores betweenthe two online user profiles to determine a sharing score between thesetwo online user profiles. In an alternative embodiment, matchingcomputing device 102 may generate a sharing score between a first userand each item in a user-listing associated with a second user bycomparing an online user profile associated with the first user and eachitem of the user-listing associated with a second online user profile.

Matching computing device 102 may receive user requests from clientdevices 106. Specifically, matching computing device 102 may receiverequests to register a user within matching system 100, or may receiverequests for initiating the matching process. For example, matchingcomputing device 102 may receive a registration request from an user,register the user within matching system 100, collect additional userdata from the user, and generate an online user profile. Matchingcomputing device 102 may further receive a request from an user toinitiate a matching process and may compare the user's online userprofile with other online user profiles based upon the request.

Exemplary Database Configuration

FIG. 2 depicts an example configuration of a database 200 (similar todatabase 112 illustrated in FIG. 1 ) included in matching system 100(shown in FIG. 1 ). Database 200 may include profile table 210,insurance table 220, third party table 230, social media table 240, usertable 250, sharing score table 260, and base score table 270. Profilerecords in profile table 210 are uniquely identified by a profileidentifier 212. Insurance records in insurance table 220 are uniquelyidentified by an insurance identifier 222. Third party records in thirdparty table 230 are uniquely identified by a third party identifier 232.Social media records in social media table 240 are uniquely identifiedby a social media identifier 242. User records in user table 250 areuniquely identified by user identifier 252. Sharing score records insharing score table 260 are uniquely identified by sharing scoreidentifier 262. Base score records in base score table 270 are uniquelyidentified by base score identifier 272.

Matching computing device 102 (shown in FIG. 1 ) is configured togenerate online user profile records. Specifically, matching computingdevice 102 is configured to generate online user profile records basedupon online user profile data 216 and profile identifier 212 in profiletable 210. In the exemplary embodiment, matching computing device 102may transmit an instruction to database 200 such that database 200generates online user profile data 216 by combining at least one ofinsurance data 226, third party data 236, social media data 246, userdata 254, sharing score data 268, and base score data 276. In analternative embodiment, matching computing device 102 generates onlineuser profile data 216 by receiving and compiling insurance data 226,third party data 236, social media data 246, user data 254, sharingscore data 268, and base score data 276. In the example embodiment,matching computing device 102 is configured to store online user profiledata 216, corresponding profile identifier 212, and corresponding useridentifier 214 in profile table 210. In the exemplary embodiment,matching computing device 102 utilizes user identifiers 214, 224, 234,244, 252, 264, 266, and 274 to aggregate data in database 200 for aparticular online user profile. In one example, user identifiers may begenerated and assigned at the time of user registration.

Matching computing device 102 is also configured to generate insurancerecords, including insurance identifier 222, in insurance table 220based upon insurance data 226 and user identifier 224. Additionally oralternatively, matching computing device 102 is configured to determineinsurance identifier 222 based upon insurance data 226 associated withuser identifier 224. In the example embodiment, matching computingdevice 102 is configured to store insurance data 226, correspondinginsurance identifier 222, and user identifier 224 as records ininsurance table 220.

Matching computing device 102 is also configured to generate third partyrecords, including third party identifier 232, in third party table 230based upon third party data 236 and user identifier 234. Additionally oralternatively, matching computing device 102 is configured to determinethird party identifier 232 based upon third party data 236 associatedwith user identifier 234. In the example embodiment, matching computingdevice 102 is configured to store third party data 236, correspondingthird party identifier 232, and user identifier 234 as records in thirdparty table 230.

Matching computing device 102 is further configured to generate socialmedia records, including social media identifier 242, in social mediatable 240 based upon social media data 246 and user identifier 244.Additionally or alternatively, matching computing device 102 isconfigured to determine social media identifier 242 based upon socialmedia data 246 associated with user identifier 244. In the exemplaryembodiment, matching computing device 102 is configured to store socialmedia data 246, corresponding social media identifier 242, and useridentifier 244 as records in social media table 240.

Matching computing device 102 is also configured to generate userrecords, including user identifier 252, in user table 250 based uponuser data 256. Additionally or alternatively, matching computing device102 is configured to determine user identifier 252 based upon user data254. In the exemplary embodiment, matching computing device 102 isconfigured to store user data 254, and corresponding user identifier 252as records in user table 250.

Matching computing device 102 is further configured to generate sharingscore records, including sharing score identifier 262, in sharing scoretable 260 based upon sharing score data 268, user identifier 264, anduser identifier 266. In the exemplary embodiment, matching computingdevice 102 may determine a sharing score between two different userscorresponding to user identifiers 264 and 266; sharing score data 268may be associated with both users and included in each user's onlineuser profile. Additionally or alternatively, matching computing device102 is configured to determine user identifiers 264 and 266 based uponsharing score data 268 associated with user identifiers 264 and 266. Inthe exemplary embodiment, matching computing device 102 is configured tostore sharing score data 268, corresponding sharing score identifier262, and corresponding user identifiers 264 and 266 as records insharing score table 260.

Matching computing device 102 may be further configured to generate basescore records, including base score identifier 272, in base score table270 based upon base score data 276 and user identifier 274. In theexemplary embodiment, matching computing device 102 may determine a basescore for an user corresponding to user identifier 274; base score data276 may be associated with the user and included in the user's onlineuser profile. Additionally or alternatively, matching computing device102 is configured to determine base score identifier 272 based upon basescore data 276 associated with user identifier 274. In the exampleembodiment, matching computing device 102 is configured to store basescore data 276, corresponding base score identifier 272, andcorresponding user identifier 274 as records in base score table 270.

Database 200 is configured to receive queries, and generate queryresponses. In some embodiments, queries include any combination ofprofile identifier 212, insurance identifier 222, third party identifier232, social media identifier 242, user identifier 252, sharing scoreidentifier 262, and base score identifier 272. Additionally oralternatively, queries may include ranges and/or rules for selectingidentifiers. Database 200 is configured to filter profile records (e.g.,online user profile data 216) based upon the query, and generate a queryresponse including the filtered data. For example, database 200 maygenerate a query response including base score data 276 corresponding toa particular user associated with user identifier 274 included in thequery.

Exemplary User Computer Device

FIG. 3 illustrates an exemplary configuration 300 of an exemplary usercomputing device 302. In some embodiments, user computing device 302 maybe in communication with a matching computing device (such as matchingcomputing device 102, shown in FIG. 1 ). User computing device 302 maybe representative of, but is not limited to client devices 106, thirdparty servers 108, social media servers 110, and/or insurance providerserver 104. For example, user computing device 302 may be a smartphone,tablet, smartwatch, wearable electronic, laptop, desktop, vehiclecomputing device, or another type of computing device associated withthe account holder.

User computer device 302 may be operated by a user 304 (e.g., a user ofmatching system 100, shown in FIG. 1 ). User computer device 302 mayreceive input from user 304 via an input device 314. User computerdevice 302 includes a processor 308 for executing instructions. In someembodiments, executable instructions may be stored in a memory area 310.Processor 308 may include one or more processing units (e.g., in amulti-core configuration). Memory area 310 may be any device allowinginformation such as executable instructions and/or transaction data tobe stored and retrieved. Memory area 310 may include one or morecomputer-readable media.

User computer device 302 also may include at least one media outputcomponent 312 for presenting information to user 304. Media outputcomponent 312 may be any component capable of conveying information touser 304. In some embodiments, media output component 312 may include anoutput adapter (not shown), such as a video adapter and/or an audioadapter. An output adapter may be operatively coupled to processor 308and operatively coupleable to an output device, such as a display device(e.g., a cathode ray tube (CRT), liquid crystal display (LCD), lightemitting diode (LED) display, or “electronic ink” display) or an audiooutput device (e.g., a speaker or headphones).

In some embodiments, media output component 312 may be configured topresent a graphical user interface (e.g., a web browser and/or a clientapplication) to user 304. A graphical user interface may include, forexample, social insurance group activity, and/or a wallet applicationfor managing payment information such as cash and/or cryptocurrencypayment methods.

In some embodiments, user computer device 302 may include input device314 for receiving input from user 304. User 304 may use input device 314to, without limitation, interact with matching system 100 (e.g., usingan app), matching computing device 102, or any of insurance providerserver 104, client devices 106, third party servers 108, and socialmedia servers 110 (shown in FIG. 1 ). Input device 314 may include, forexample, a keyboard, a pointing device, a mouse, a stylus, and/or atouch sensitive panel (e.g., a touch pad or a touch screen). A singlecomponent, such as a touch screen, may function as both an output deviceof media output component 312 and input device 314. User computer device302 may further include at least one sensor, including, for example, agyroscope, an accelerometer, a position detector, a biometric inputdevice, a telematics data collection device, and/or an audio inputdevice. In some embodiments, at least some data collected by usercomputer device 302 may be transmitted to insurance provider 112. In theexemplary embodiment, data collected by user computer device 302 may beincluded in online user profiles.

User computer device 302 may also include a communication interface 316,communicatively coupled to any of matching computing device 102,insurance provider server 104, client devices 106, third party servers108, and social media servers 110. Communication interface 316 mayinclude, for example, a wired or wireless network adapter and/or awireless data transceiver for use with a mobile telecommunicationsnetwork.

Stored in memory area 310 may be, for example, computer-readableinstructions for providing a user interface to user 304 via media outputcomponent 312 and, optionally, receiving and processing input from inputdevice 314. The user interface may include, among other possibilities, aweb browser and/or a client application. Web browsers enable users, suchas user 304, to display and interact with media and other informationtypically embedded on a web page or a website hosted by insuranceprovider server 106 and/or user computing device 302. A clientapplication may allow user 304 to interact with, for example, any ofmatching computing device 102, insurance provider server 104, clientdevices 106, third party servers 108, and social media servers 110. Forexample, instructions may be stored by a cloud service and the output ofthe execution of the instructions sent to the media output component312.

Exemplary Server Device

FIG. 4 depicts an exemplary configuration 400 of an exemplary servercomputing device 402, in accordance with one embodiment of the presentdisclosure. Server computer device 402 may include, but is not limitedto, matching computing device 102 (shown in FIG. 1 ). Server computerdevice 402 may include a processor 405 for executing instructions.Instructions may be stored in a memory area 410. Processor 405 mayinclude one or more processing units (e.g., in a multi-coreconfiguration).

Processor 405 may be operatively coupled to a communication interface415 such that server computer device 402 may be capable of communicatingwith a remote device such as another server computer device 402 or auser computing device, such as user computing device 302 (shown in FIG.3 ). For example, communication interface 415 may receive requests fromor transmit requests to user computing device 302 via the Internet.

Processor 405 may also be operatively coupled to a storage device 425.Storage device 425 may be any computer-operated hardware suitable forstoring and/or retrieving data, such as, but not limited to, dataassociated with database 112 (shown in FIG. 1 ). In some embodiments,storage device 425 may be integrated in server computer device 402. Forexample, server computer device 402 may include one or more hard diskdrives as storage device 425. In other embodiments, storage device 425may be external to server computer device 402 and may be accessed by aplurality of server computer devices 402. For example, storage device425 may include a storage area network (SAN), a network attached storage(NAS) system, and/or multiple storage units such as hard disks and/orsolid state disks in a redundant array of inexpensive disks (RAID)configuration.

In some embodiments, processor 405 may be operatively coupled to storagedevice 420 via a storage interface 420. Storage interface 420 may be anycomponent capable of providing processor 405 with access to storagedevice 420. Storage interface 420 may include, for example, an AdvancedTechnology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, aSmall Computer System Interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any component providing processor 405with access to storage device 420.

Processor 405 executes computer-executable instructions for implementingaspects of the disclosure. In some embodiments, processor 405 may betransformed into a special purpose microprocessor by executingcomputer-executable instructions or by otherwise being programmed.

Exemplary Graphical User Interface

FIG. 5 illustrates graphical user interface (“GUI”) 502 presented onuser computing device 500. GUI 502 may be generated and transmitted bymatching computing device 102 (shown in FIG. 1 ) and may provide a meansfor a user to input user data, view other online user profiles, and/oraccess sharing scores calculated between the user and another user. Usercomputing device 500 includes communication interface 526, which allowsuser computing device 500 to receive and transmit data wirelessly orthrough wires. In some embodiments, the GUI 502 may be integrated in asocial media website, and the GUI 502 may display the potential flowfrom a renter's perspective when looking for a vehicle to rent, withrenters and vehicle owners be best matched via a sharing score algorithmor determination.

GUI 502 is configured to display list page 504. List page 504 displayssearch results to a “searching user.” In the exemplary embodiment,search results include user-listings associated with a “listing user.”In some embodiments, search results may include any data associated withthe user and/or other online user profiles, such as user data, insurancedata, third party data, social media data, base scores, and/or sharingscores. For example, search results may take the form of vehicles listedfor rent by other users. As another example, search results may showcars and additional user data such as a base score and/or a sharingscore. In yet another example, search results may show other types ofdata other than user-listings, such as a user profile that matches aname searched by a user.

List page 504 includes search bar 506, which allows the searching userto input search terms and search for data containing those search terms.Specifically, a searching user may enter search terms, and matchingcomputing device 102 may search database 112 (shown in FIG. 1 ) forprofiles that include data matching the search term. For example, a usermay input the search term “Toyota Corolla.” List page 504 maysubsequently display a list of items that match the term “ToyotaCorolla.” List page 504 also includes map view link 508, which causesGUI 502 to display map page 604 (shown in FIG. 6 ). In an alternativeembodiment, map view link 506 causes GUI 502 to display a map includingsearch results as a component of list page 504. List page 504 alsoincludes filters 510, which allow the user to include or exclude searchresults based upon certain criteria. List page 504 also displays currentfilters 512, which indicate criteria set by the user through filters510. List page 504 also includes notifications toggle 514, which allowsthe user to toggle automatic notifications on and off, and informationbutton 516, which displays additional information to the user.

In the exemplary embodiment, list page 504 includes results list 518.Results list 518 may contain one or more search results 520. Searchresults 520 are items associated with online user profiles that matchcriteria set in filters 510, search terms entered in search bar 506,and/or other criteria set by a searching user in his/her online userprofile. In the exemplary embodiment, search results 520 areuser-listings that match criteria set by the searching user. Forexample, search results 520 may include a plurality of rental carslisted as “for rent” (e.g., rental listing). Further, based upon userpreferences or filters set by the searching user, list page 504 maydisplay only certain rental cars in search results 520 that matchspecific criteria, such as “fewer than 30,000 miles” or “no prioraccidents.” In some embodiments, results list 518 may contain no searchresults. For example, if no rental listings match user-input criteria,there may be no search results to display.

Matching computing device 102 may generate search results 520, transmitsearch results 520 to user computing device 500, and instruct usercomputing device 500 to display search results 520 through GUI 502.Search results 520 may include data gathered from online user profilesof listing users (e.g., owner profiles of owners), such as insurancedata, user data, third party data, and social media data. Further,search results 520 may include data calculated from the above referenceddata, such as a base score calculated for an owner profile, a sharingscore calculated between a searching user (e.g., a renter) and a listinguser, and/or a sharing score calculated between the searching user andeach item included in a user-listing associated with a listing user. Anyof the above referenced data may be compiled by matching computingdevice 102 and displayed in search results 520.

In the exemplary embodiment, search results 520 contain more data thanis displayed on list page 504. In other words, search results 520 arepresented such that certain information, such as a sharing score, isdisplayed on list page 504, but other information related to the searchresult 520 is not presented visually. For example, GUI 502 may displaysearch results 520 such that an image of the user-listing along with thebase score of the listing user is displayed; however, when searchresults 520 are generated, additional data such as user-data andinsurance data associated with search results 520 are not displayed.This aggregation of search results 520 and certain data associated withsearch results 520 provides a convenient, understandable interface for asearching user to locate user-listings that match her criteria.

In the exemplary embodiment, list page 504 also includes result image522 and details 524. Each search result 520 may include a result image522 and details 524. List page 504 allows a user to select the resultimage 522 or details 524 in order to receive more information aboutsearch result 520. Specifically, a user may select the result image 522and/or details 524, causing GUI 502 to display listing user profile page704 (see FIG. 7 ). In another embodiment, selecting result image 522and/or details 524 causes GUI 502 to display details page 804 (see FIG.8 ). In yet another embodiment, selecting result image 522 and/ordetails 524 causes GUI 502 to display additional information within listpage 504.

Exemplary Graphical User Interface

FIG. 6 illustrates graphical user interface (“GUI”) 502 presented onuser computing device 500. GUI 502 may be generated and transmitted bymatching computing device 102 (shown in FIG. 1 ) and may provide a meansfor a user to input user data, view other online user profiles, and/oraccess sharing scores calculated between the user and an another user.User computing device 500 includes communication interface 526, whichallows user computing device 500 to receive and transmit data wirelesslyor through wires. In one embodiment, GUI 502 may again be a view fromthe renter's perspective, and once a renter selects the vehicle he orshe wishes to use, they are able to view availability, location, andvehicle owners profile for user feedback, etc. From here, the vehicleowner and renter may agree on pick up and drop off locations and times.

GUI 502 is configured to display map page 604. Map page 604 displayssearch results to a “searching user.” In the exemplary embodiment,search results include user-listings associated with a “listing user.”In some embodiments, search results may include any data associated withthe user or other online user profiles, such as user data, insurancedata, third party data, social media data, base scores, and/or sharingscores. For example, search results may take the form of vehicles listedfor rent by other users. As another example, search results may showvehicles and additional user data such as a base score and/or a sharingscore. In yet another example, search results may show other types ofdata other than user-listings, such as an online user profile thatmatches a name searched by a user.

Map page 604 includes search bar 506, which allows a searching user toinput search terms and search for data containing those search terms.Specifically, a searching user may enter search terms, and matchingcomputing device 102 may search database 112 (shown in FIG. 1 ) forprofiles that include data matching the search term. For example, a usermight enter the search term “Toyota Corolla” and map page 604 maysubsequently display a map of items that match the term “ToyotaCorolla.” Map page 604 also includes list view link 606, which causesGUI 502 to display list page 504 (shown in FIG. 5 ). In an alternativeembodiment, list view link 606 causes GUI 502 to display a listincluding search results as a component of map page 604.

Map page 604 also includes filters 510, which allows the user to includeor exclude search results based upon certain criteria. Map page 604 alsodisplays current filters 512, which indicate criteria set by the userthrough filters 510. Map page 604 also includes notifications toggle514, which allows a user to toggle automatic notifications on and off,and information button 516, which displays additional information to auser.

In the exemplary embodiment, map page 604 includes results list 612.Results list 612 may contain one or more search results 614. Searchresults 614 are items related to online user profiles that matchcriteria set in filters 510, search terms entered in search bar 506,and/or other criteria set by a searching user in his/her online userprofile. In the exemplary embodiment, search results 614 areuser-listings that match criteria set by the searching user. Forexample, search results 614 may include a plurality of rental carslisted by listing users as “for rent.” Further, based upon userpreferences or filters set by the searching user, map page 604 maydisplay only certain rental cars in search results 614 that matchspecific criteria, such as “fewer than 30,000 miles” or “no prioraccidents.” In some embodiments, results list 612 may contain no searchresults. For example, if no rental listings match user-input criteria,there may be no search results to display.

Matching computing device 102 may generate search results 614, transmitsearch results 614 to user computing device 500, and instruct usercomputing device 500 to display search results 614 through GUI 502.Search results 614 may contain data gathered from online user profilesof listing users, such as insurance data, user data, third party data,and social media data. Further, search results 614 may contain datacalculated based upon the above referenced data, such as a base scorecalculated for a listing user and/or a sharing score calculated betweenthe searching user and a listing user. Any of the above referenced datamay be compiled by matching computing device 102 and displayed in searchresults 614. In the exemplary embodiment, search results 614 containmore data than is displayed on map page 604. In other words, searchresults 614 are presented such that certain information, such as vehiclemake and model, is displayed on map page 604, but other informationrelated to the search results 614 is not presented visually. Thisaggregation of search results 614 and certain data associated withsearch results 614 provides a convenient, understandable interface for asearching user to locate user-listings that match her criteria.

In the exemplary embodiment, map page 604 includes results map 606,which includes search results 614 overlaid on a map. Specifically, aresult icon 608 is assigned to each search result 614, and result icons608 are visually overlaid on a map. Matching computing device 102 (shownin FIG. 1 ) may generate results map 606 by determining search results614, determining a location associated with each search result 614,assigning a result icon 608 to each search result 614, and overlayingresult icons 608 on a map based upon the locations of search results614. Results map 606 may be configured to display results image 610associated with one of the search results 614. Map page 604 may beconfigured to display results image 610 upon user selection of one ofsearch icons 608.

In another embodiment, map page 604 may be configured to display resultsimage 610 upon user selection of one of the search results 614 fromsearch list 612. In the exemplary embodiment, map page 604 may acceptuser input which causes results map 606 to scroll, move, enlarge,shrink, zoom, rotate, or otherwise change the location and/ororientation of the area displayed by results map 606.

In the exemplary embodiment, result icons 608 may have a certainappearance based upon data stored in search results 614. In other words,based upon attributes of search result 614, the corresponding resulticon 608 will be assigned a certain appearance. For example, searchresults with a sharing score above a certain value may be assigned aresult icon that is a red square. In another example, search resultswith a base score below a certain value may be assigned a result iconthat is a green circle. In another example, search results that matchparticular user-defined criteria may be assigned a result icon that is agold star.

Map page 604 includes score legend 616, which displays the criteria fordetermining the appearance of the result icons. In other words, scorelegend 616 allows the user to understand what the different result icons608 indicate. By associating search results 614 with result icons 608and accurately locating result icons 608 on a map, map page 604 providesa convenient, understandable way for a user to view user-listingsavailable in a particular geographical area.

In one embodiment, user selection of results image 610, results icons608, and/or search results 614 may cause GUI 502 to display user profilepage 704 (see FIG. 7 ), details page 804 (see FIG. 8 ), or additionalinformation within map page 604.

Exemplary Graphical User Interface

FIG. 7 illustrates graphical user interface (“GUI”) 502 presented onuser computing device 500. GUI 502 may be generated and transmitted bymatching computing device 102 (shown in FIG. 1 ) and may provide a meansfor a user to input user data, view other online user profiles, and/oraccess sharing scores calculated between the user and an another user.User computing device 500 includes communication interface 526, whichallows user computing device 500 to receive and transmit data wirelesslyor through wires.

GUI 502 is configured to display user profile page 704. User profilepage 704 contains information about a user, such as user data, insurancedata, social media data, third party data, base scores, and/or sharingscores associated with a user. In one embodiment, a “searching user” mayaccess a user profile page associated with a “listing user” by selectingthat listing user or a search result associated with the listing user.For example, a searching user may select a rental car from searchresults list 520 (shown in FIG. 5 ) and be directed to a user profilepage associated with the listing user who owns the rental car. Inanother embodiment, a searching user may access his/her own profilepage. In one embodiment, user profile page 704 is a separate pagegenerated by GUI 502 and displayed to a user. In another embodiment,user profile page 704 is generated by GUI 502 and overlaid on anotherinterface page, such as list page 504 or map page 604 (shown in FIGS. 5and 6 respectively).

User profile page 704 includes online user profile 706. Online userprofile 706 may include and display information about a user, such asuser data, insurance data, social media data, third party data, and/orbase scores associated with a user. In the exemplary embodiment, onlineuser profile 706 may also include a sharing score between a first userviewing profile 706 and a second user associated with online userprofile 706. Matching computing device 102 (shown in FIG. 1 ) maygenerate online user profile 706 based upon information received fromdatabase 112 (shown in FIG. 1 ).

Online user profile 706 may also include one or more user-listings, suchas user-listing 708. User-listing 708 includes an item or service listedby the user and may be the search result that caused online user profile706 to be displayed to the searching user. In some embodiments, onlineuser profile 706 includes a plurality of user-listings 708. Online userprofile 706 may also include a sharing score between a first userviewing profile 706 and each user listing 708 associated with profile706. In some embodiments, user listing 708 can be selected by thesearching user, causing the GUI to generate and display list page 504,map page 604, or details page 804.

User profile page 704 may include additional information 710, which mayinclude any of the possible data for 706, as well as additionalinformation not directly associated with the listing user. For example,additional information 710 may include a sharing score or a sellerrating. In another example, additional information 710 may include ageneral notice to all users. Additional information 710 may also includepreferences option 712, which allows a user to set certain preferencesassociated with his/her user profile page 704. For example, a user mayset preferences which hide certain information from other users.

Exemplary Graphical User Interface

FIG. 8 illustrates graphical user interface (“GUI”) 502 presented onuser computing device 500. GUI 502 may be generated and transmitted bymatching computing device 102 (shown in FIG. 1 ) and may provide a meansfor a user to input user data, view other online user profiles, and/oraccess sharing scores calculated between the user and an another user.User computing device 500 includes communication interface 526, whichallows user computing device 500 to receive and transmit data wirelesslyor through wires.

GUI 502 is configured to display details page 804. Details page 804includes additional information and actions associated with a particularsearch result, such as search results 520 and 614 (shown in FIGS. 5 and6 respectively). In the exemplary embodiment, search results includeuser-listings associated with a “listing user.” In some embodiments,search results may include any data associated with the user or otheronline user profiles, such as user data, insurance data, third partydata, social media data, base scores, and/or sharing scores. Forexample, search results may take the form of cars listed for rent byother users. As another example, search results may show cars andadditional user data such as a base score and/or a sharing score. In yetanother example, search results may show other types of data other thanuser-listings, such as an online user profile that matches a namesearched by a user.

In one embodiment, details page 804 contains item image 806, whichcontains an image of a search result selected by a searching user. Insome embodiments, item image 806 contains multiple images associatedwith a search result, and a user is able to scroll through multipleimages. For example, item image 806 may be an image of a car listed forrent by a listing user. Further, a user may be able to swipe or navigatethrough multiple images of the car.

In one embodiment, details page 804 also includes listing information808. Matching computing device 102 may generate listing information 808based upon data received from database 112 (shown in FIG. 1 ). Listinginformation 808 may include any data associated with a search result. Inthe exemplary embodiment, the item listed is a rental car, and thelisting information 808 includes rates for renting the car, a sharingscore between the searching user and the listing user, and marketplaceactivity of the listing user. Listing information 808 may include anycombination of available data.

In the exemplary embodiment, details page 804 also includes availabilitylink 810. A user may select availability link 810, causing GUI 502 togenerate and display additional information regarding the availabilityof the listing. For example, availability link 810 might cause GUI 502to display a listing of dates which a rental car is available forrental. In one embodiment, availability link 810 may cause GUI 502 toredirect the user to an additional page which details the availabilityof the item. In another embodiment, availability link 810 may cause GUI502 to display availability information within details page 804.

In the exemplary embodiment, matching computing device 102 may associatelisting information 808 with availability link 810, such that datadisplayed through listing information 808 may depend on dates specifiedvia availability link 810. Specifically, details page 804 may allow auser to select at least one date through availability link 810 and mayupdate listing information 808 based upon the date selection. Forexample, a user may select a date via availability link 810, and detailspage may update listing information score 808 such that displayed ratesreflect the rates for the specific date selected by the user.

In the exemplary embodiment, details page 804 also includes map link812. A user may select map link 812, causing GUI 502 to generate anddisplay additional information regarding the location of the listing. Inone embodiment, map link 812 causes GUI 502 to redirect the user to mappage 604. In another embodiment, map link 812 may cause GUI 502 todisplay additional location information within details page 804. Forexample, map link 812 may cause GUI 502 to display a map which visuallyindicates the location of the vehicle.

In the exemplary embodiment, details page 804 also includes additionalactions 814. A user may select one of additional actions 814, causingGUI 502 to generate and display a field for taking additional actions.For example, selecting an additional action called “message” may bringup a field for typing and sending a message to the “listing user” whoposted the listing. As another example, an additional action called“share” may bring up a field for typing in a message and selecting arecipient for the message and a link to the listing page. As anotherexample, an additional action called “more” may display a list offurther options the user can select from.

In another embodiment, selecting one of additional actions 814 causesGUI 502 to carry out a response. For example, selecting an additionalaction called “save” may cause GUI 502 to download the listing to astorage device local to user computing device 500 or may cause matchingcomputing device 102 to save the listing in database 112.

Exemplary Computer-Implemented Method

FIG. 9 depicts a flow chart illustrating a computer-implemented method900 for determining a sharing score between online user profiles. In theexemplary embodiment, method 900 may be implemented by a matchingcomputer system such as matching computer system 100 (shown in FIG. 1 ),and more specifically, by a matching computing device 102 (shown in FIG.1 ).

Method 900 may include generating 902 a first online user profileassociated with a first user, wherein the first online user profilecomprises at least one of first insurance data, first social media data,first third party data, first user data, and first sharing dataassociated with a first user. Method 900 may also include generating 904a second online user profile associated with a second user, wherein thesecond online user profile comprises at least one of second insurancedata, second social media data, second third party data, second userdata, and second sharing data associated with a second user.

Method 900 may further include calculating 906 a first base scoreassociated with the first user based upon the first online user profile,wherein the first base score represents a first level of trustworthinessof the first user. Method 900 may additionally include calculating 908 asecond base score associated with the second user based upon the secondonline user profile wherein the second base score represents a secondlevel of trustworthiness of the second user. Method 900 may also includedetermining 910 a sharing score between the first and second users basedupon the first and second base scores, wherein the sharing scorerepresents a level of matching between the first online user profile andthe second online user profile.

Exemplary Computer Device

FIG. 10 depicts a diagram 1000 of components of one or more exemplarycomputing devices 1010 that may be used in matching system 100 (shown inFIG. 1 ). In some embodiments, computing device 1010 may be similar tomatching computing device 102 (shown in FIG. 1 ). Database 1020 may becoupled with several separate components within computing device 1010,which perform specific tasks. In this embodiment, database 1020 mayinclude user data 1021, insurance data 1022, third party data 1023,social media data 1024, sharing data 1025, base score data 1026, andsharing score data 1027. In some embodiments, database 1020 is similarto database 112 (shown in FIG. 1 ).

Computing device 1010 may include the database 1020, as well as datastorage devices 1030. Computing device 1010 may also include ananalytics component 1040 for generating online user profiles 902, 904,calculating base scores 906, 908, and determining sharing scores 910(all shown in FIG. 9 ). Computing device 1010 may further includedisplay component 1050 for generating and displaying a graphical userinterface, such as GUI 502 (shown in FIGS. 5-8 ). Moreover, computingdevice 1010 may include communications component 1060 for receiving andtransmitting data, such as user data 1021, insurance data 1022, thirdparty data 1023, social media data 1024, sharing data 1025, base scoredata 1026, and sharing score data 1027.

Exemplary Embodiments & Functionality

The present embodiments may relate to systems and methods fordetermining a sharing score between online user profiles. The system mayinclude a matching computing device, one or more insurance providerservers, one or more client systems, one or more social media servers,one or more third party servers, and/or one or more databases, asdescribed above.

In one aspect, a matching computer system for determining a sharingscore between online user profiles is provided. The matching computersystem may include at least one processor in communication with at leastone memory device. The at least one processor may be configured to: (i)generate a first online user profile associated with a first user,wherein the first online user profile comprises at least one of firstinsurance data, first social media data, first third party data, firstuser data, and first sharing data associated with a first user; (ii)generate a second online user profile associated with a second user,wherein the second online user profile comprises at least one of secondinsurance data, second social media data, second third party data,second user data, and second sharing data associated with a second user;(iii) calculate a first base score associated with the first user basedupon the first online user profile, wherein the first base scorerepresents a first level of trustworthiness of the first user; (iv)calculate a second base score associated with the second user based uponthe second online user profile wherein the second base score representsa second level of trustworthiness of the second user; and/or (v)determine a sharing score between the first and second users based uponthe first and second base scores, wherein the sharing score represents alevel of matching between the first online user profile and the secondonline user profile. The matching computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

For instance, one enhancement may be where the at least one processormay be further configured to: (i) receive a first user-type associatedwith the first user and a second user-type associated with the seconduser; (ii) calculate the first base score based upon the firstuser-type; and/or (iii) calculate the second base score based upon thesecond user-type.

Another enhancement may be where the at least one processor may befurther configured to: generate a graphical user interface, wherein thegraphical user interface is configured to: (i) display the first basescore and the second base score; and/or (ii) display the sharing scorebetween the first user and the second user.

In another aspect, a matching computer system for determining a sharingscore between online user profiles is provided. The matching computersystem may include at least one processor in communication with at leastone memory device. The at least one processor may be configured to: (i)receive a user-listing associated with a first user, wherein theuser-listing includes a plurality of items offered for at least sale,rent, and lease, and wherein the first user is associated with a firstuser-type corresponding to an owner; (ii) calculate an item base scorefor each of the plurality of items, each item base score calculatedbased upon a first online user profile associated with the first user;(iii) calculate a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and/or(iv) determine a sharing score between each of the plurality of itemsand the second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile. The matching computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

For instance, one enhancement may be where the user-listing includeslisting availability dates and listing rates associated with the listingavailability dates.

Another enhancement may be where the at least one processor may befurther configured to: generate a graphical user interface, wherein thegraphical user interface is configured to: (i) display the user-listingassociated with the first user; (ii) receive a first selection inputfrom the second user, the first selection input indicating at least oneselected item from the user-listing; and/or (iii) display, in responseto receiving the first selection input, the sharing score between the atleast one selected item and the second user.

Another enhancement may be where the at least one processor may befurther configured to generate a graphical user interface, wherein thegraphical user interface is configured to: (i) display, in response toreceiving the first selection input, at least one listing availabilitydate and the associated listing rate; (ii) receive a second selectioninput from the second user, the second selection input indicating anavailable date; and/or (iii) concurrently display a location on a map ofthe at least one selected item and details associated with the at leastone selected item.

Another enhancement may be where the at least one processor may befurther configured to: generate a graphical user interface, wherein thegraphical user interface is configured to display the user-listing andeach determined sharing score between each of the plurality of items andthe second user.

Another enhancement may be where the plurality of items includes atleast one of goods and services.

In another aspect, a computer-implemented method for determining asharing score between online user profiles is provided. The method maybe implemented by a matching computer system including at least oneprocessor. The method may include: (i) generating a first online userprofile associated with a first user, wherein the first online userprofile comprises at least one of first insurance data, first socialmedia data, first third party data, first user data, and first sharingdata associated with a first user; (ii) generating a second online userprofile associated with a second user, wherein the second online userprofile comprises at least one of second insurance data, second socialmedia data, second third party data, second user data, and secondsharing data associated with a second user; (iii) calculating a firstbase score associated with the first user based upon the first onlineuser profile, wherein the first base score represents a first level oftrustworthiness of the first user; (iv) calculating a second base scoreassociated with the second user based upon the second online userprofile wherein the second base score represents a second level oftrustworthiness of the second user; and/or (v) determining a sharingscore between the first and second users based upon the first and secondbase scores, wherein the sharing score represents a level of matchingbetween the first online user profile and the second online userprofile. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

For instance, one enhancement may be where the computer-implementedmethod may further include: (i) receiving a first user-type associatedwith the first user and a second user-type associated with the seconduser; (ii) calculating the first base score based upon the firstuser-type; and/or (iii) calculating the second base score based upon thesecond user-type.

A further enhancement may be where the computer-implemented method mayfurther include: generating a graphical user interface, wherein thegraphical user interface is configured to: (i) display the first basescore and the second base score; and/or (ii) display the sharing scorebetween the first user and the second user.

In another aspect, a computer-implemented method for determining asharing score between online user profiles is provided. The method maybe implemented by a matching computer system including at least oneprocessor. The method may include: (i) receiving a user-listingassociated with a first user, wherein the user-listing includes aplurality of items offered for at least sale, rent, and lease, andwherein the first user is associated with a first user-typecorresponding to an owner; (ii) calculating an item base score for eachof the plurality of items, each item base score calculated based upon afirst online user profile associated with the first user; (iii)calculating a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and/or(iv) determining a sharing score between each of the plurality of itemsand the second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile. The method may include additional, less,or alternate actions, including those discussed elsewhere herein.

For instance, one enhancement may be where the user-listing includeslisting availability dates and listing rates associated with the listingavailability dates.

A further enhancement may be where the computer-implemented method mayfurther include: generating a graphical user interface, wherein thegraphical user interface may be configured to: (i) display theuser-listing associated with the first user; (ii) receive a firstselection input from the second user, the first selection inputindicating at least one selected item from the user-listing; and/or(iii) display, in response to receiving the first selection input, thesharing score between the at least one selected item and the seconduser.

A further enhancement may be where the computer-implemented method mayfurther include: generating a graphical user interface, wherein thegraphical user interface may be configured to: (i) display, in responseto receiving the first selection input, at least one listingavailability date and the associated listing rate; (ii) receive a secondselection input from the second user, the second selection inputindicating an available date; and/or (iii) concurrently display alocation on a map of the at least one selected item and detailsassociated with the at least one selected item.

A further enhancement may be where the computer-implemented method mayfurther include generating a graphical user interface, wherein thegraphical user interface is configured to display the user-listing andeach determined sharing score between each of the plurality of items andthe second user.

In another aspect, at least one non-transitory computer-readable storagemedia having computer-executable instructions embodied thereon fordetermining a sharing score between online user profiles is provided.When executed by at least one processor, the computer-executableinstructions may cause the processor to: (i) receive a user-listingassociated with a first user, wherein the user-listing includes aplurality of items offered for at least sale, rent, and lease, andwherein the first user is associated with a first user-typecorresponding to an owner; (ii) calculate an item base score for each ofthe plurality of items, each item base score calculated based upon afirst online user profile associated with the first user; (iii)calculate a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter; and/or(iv) determine a sharing score between each of the plurality of itemsand the second user, wherein each sharing score is based upon eachrespective item base score and the second user base score, and whereineach sharing score represents a level of matching between each item andthe second online user profile. The instructions may direct additional,less, or alternate functionality, including that discussed elsewhereherein.

For instance, one enhancement may be where the computer-executableinstructions may further cause the processor to: (i) receive a firstuser-type associated with the first user and a second user-typeassociated with the second user; (ii) calculate the first base scorebased upon the first user-type; and/or (iii) calculate the second basescore based upon the second user-type.

Another enhancement may be where the computer-executable instructionsmay further cause the processor to: generate a graphical user interface,wherein the graphical user interface is configured to: (i) display thefirst base score and the second base score; and/or (ii) display thesharing score between the first user and the second user.

In another aspect, at least one non-transitory computer-readable storagemedia having computer-executable instructions embodied thereon fordetermining a sharing score between online user profiles is provided.When executed by at least one processor, the computer-executableinstructions may cause the processor to: (i) generate a first onlineuser profile associated with a first user, wherein the first online userprofile comprises at least one of first insurance data, first socialmedia data, first third party data, first user data, and first sharingdata associated with a first user; (ii) generate a second online userprofile associated with a second user, wherein the second online userprofile comprises at least one of second insurance data, second socialmedia data, second third party data, second user data, and secondsharing data associated with a second user; (iii) calculate a first basescore associated with the first user based upon the first online userprofile, wherein the first base score represents a first level oftrustworthiness of the first user; (iv) calculate a second base scoreassociated with the second user based upon the second online userprofile wherein the second base score represents a second level oftrustworthiness of the second user; and (v) determine a sharing scorebetween the first and second users based upon the first and second basescores, wherein the sharing score represents a level of matching betweenthe first online user profile and the second online user profile. Theinstructions may direct additional, less, or alternate functionality,including that discussed elsewhere herein.

For instance, one enhancement may be where the user-listing includeslisting availability dates and listing rates associated with the listingavailability dates.

Another enhancement may be where the computer-executable instructionsmay further cause the processor to: generate a graphical user interface,wherein the graphical user interface is configured to: (i) display theuser-listing associated with the first user; (ii) receive a firstselection input from the second user, the first selection inputindicating at least one selected item from the user-listing; and/or(iii) display, in response to receiving the first selection input, thesharing score between the at least one selected item and the seconduser.

Another enhancement may be where the computer-executable instructionsmay further cause the processor to: generate a graphical user interface,wherein the graphical user interface is configured to: (i) display, inresponse to receiving the first selection input, at least one listingavailability date and the associated listing rate; (ii) receive a secondselection input from the second user, the second selection inputindicating an available date; and/or (iii) concurrently display alocation on a map of the at least one selected item and detailsassociated with the at least one selected item.

Another enhancement may be where the computer-executable instructionsmay further cause the processor to: generate a graphical user interface,wherein the graphical user interface is configured to display theuser-listing and each determined sharing score between each of theplurality of items and the second user.

Another enhancement may be where the plurality of items includes atleast one of goods and services.

Machine Learning & Other Matters

The computer systems and computer-implemented methods discussed hereinmay include additional, less, or alternate actions and/orfunctionalities, including those discussed elsewhere herein. Thecomputer systems may include or be implemented via computer-executableinstructions stored on non-transitory computer-readable media. Themethods may be implemented via one or more local or remote processors,transceivers, servers, and/or sensors (such as processors, transceivers,servers, and/or sensors mounted on mobile computing devices, orassociated with smart infrastructure or remote servers), and/or viacomputer executable instructions stored on non-transitorycomputer-readable media or medium.

In some embodiments, a matching computing device is configured toimplement machine learning, such that the matching computing device“learns” to analyze, organize, and/or process data without beingexplicitly programmed. Machine learning may be implemented throughmachine learning methods and algorithms (“ML methods and algorithms”).In an exemplary embodiment, a machine learning module (“ML module”) isconfigured to implement ML methods and algorithms. In some embodiments,ML methods and algorithms are applied to data inputs and generatemachine learning outputs (“ML outputs”). Data inputs may include but arenot limited to: user data, third party data, social media data,insurance data, sharing data, base score data, and/or sharing scoredata. ML outputs may include but are not limited to: sharing data, basescore data, sharing score data, search results, and/or mapped searchresults. In some embodiments, data inputs may include certain MLoutputs.

In some embodiments, at least one of a plurality of ML methods andalgorithms may be applied, which may include but are not limited to:linear or logistic regression, instance-based algorithms, regularizationalgorithms, decision trees, Bayesian networks, cluster analysis,association rule learning, artificial neural networks, deep learning,dimensionality reduction, and support vector machines. In variousembodiments, the implemented ML methods and algorithms are directedtoward at least one of a plurality of categorizations of machinelearning, such as supervised learning, unsupervised learning, andreinforcement learning.

In one embodiment, the ML module employs supervised learning, whichinvolves identifying patterns in existing data to make predictions aboutsubsequently received data. Specifically, the ML module is “trained”using training data, which includes example inputs and associatedexample outputs. Based upon the training data, the ML module maygenerate a predictive function which maps outputs to inputs and mayutilize the predictive function to generate ML outputs based upon datainputs. The example inputs and example outputs of the training data mayinclude any of the data inputs or ML outputs described above. Forexample, a ML module may receive training data comprising social mediadata, insurance data, and a base score associated with the social mediadata and insurance data. The ML module may then generate a model whichmaps base scores to aspects of social media data and insurance data. TheML module may then generate base scores as a ML output based uponsubsequently received social media data and insurance data.

In another embodiment, an ML module may employ unsupervised learning,which involves finding meaningful relationships in unorganized data.Unlike supervised learning, unsupervised learning does not involveuser-initiated training based upon example inputs with associatedoutputs. Rather, in unsupervised learning, the ML module may organizeunlabeled data according to a relationship determined by at least one MLmethod/algorithm employed by the ML module. Unorganized data may includeany combination of data inputs and/or ML outputs as described above. Forexample, a ML module may receive unlabeled data comprising user data,social media data, and sharing scores between users. The ML module mayemploy an unsupervised learning method such as “clustering” to identifypatterns and organize the unlabeled data into meaningful groups. Thenewly organized data may be used, for example, to generate a model whichassociates user data and social media data to sharing scores.

In yet another embodiment, a ML module may employ reinforcementlearning, which involves optimizing outputs based upon feedback from areward signal. Specifically, the ML module may receive a user-definedreward signal definition, receive a data input, utilize adecision-making model to generate a ML output based upon the data input,receive a reward signal based upon the reward signal definition and theML output, and alter the decision-making model so as to receive astronger reward signal for subsequently generated ML outputs. The rewardsignal definition may be based upon any of the data inputs or ML outputsdescribed above. For example, a ML module may implement reinforcementlearning in generating sharing scores between users. The ML module mayutilize a decision-making model to generate sharing scores between usersbased upon base scores and social media data, and may further receiveuser-satisfaction data indicating a level of satisfaction experienced bytwo users who engaged in a transaction. A reward signal may be generatedby comparing the user-satisfaction data to the sharing score between thetwo users. Based upon the reward signal, the ML module may update thedecision-making model such that subsequently generated sharing scoresmore accurately predict user satisfaction.

Exemplary P2P Rental Vehicle Transaction

FIG. 11 illustrates an exemplary computer-implemented method ofconducting a P2P rental vehicle transaction 1100. Thecomputer-implemented method 1100 may be conducted by one or more localor remote processors, servers, transceivers, and/or sensors, and/ordirected by computer executable instructions stored on non-transitorycomputer readable media or medium.

The method 1100 may include (vehicle or other types of) renters and(vehicle or other types of) owners signing up to use a sharing platform,server, or website via their mobile devices or other computing devices1102. The renters and owners may each provide affirmative consent orpermission for a remote server to gather or collect various types ofdata, such as telematics data, insurance data, credit score data, socialmedia data, social media activity data, and other types of data,including those discussed elsewhere herein. The permission to collectdata may be received, via one or more processors, transceivers, and/orremote servers, and via wireless communication or data transmission frommobile devices.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), collecting telematicsdata for each renter or prospective renter, and generating a risk ordriver score or risk or driving profile for each renter based upon thetelematics data 1104. The telematics data may include cornering,braking, speed, acceleration, deceleration, location, heading, route,following distance information, time-of-day, and other types oftelematics data associated with driving behavior of the renter.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), collecting social mediadata and/or social media activity data for each renter or prospectiverenter, and generating a social media score or profile for each renterbased upon the social media or social media activity data 1104. Thesocial media or social media activity data may include parameters orfactors discussed elsewhere herein, such as likes or dislikes, number ofonline friends, how long the users have been on a social media platform,number of posts, the degree to which the dependent people are connectedand history on the marketplace, etc. The method 1100 may also includecomputing a trust score based upon the generated social media score.

The method 1100 may include, the renter subsequently logging into theplatform or website, and via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), entering the desiredcharacteristics of a rental items, in this case, a rental vehicle 1108.The rental vehicle characteristics may include make, model, year, color,etc. The renter may also enter a rental period and desired amount to pay1108.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, retrieving the renter's driving or driver scoreor profile and/or the renter's social media score or profile 1110 from amemory unit.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, identifying rental vehicles that have therenter's desired characteristics within a predetermined distance, andthat are available during or at the rental period 1112.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, for each rental vehicle that possesses therenter's desired characteristics, verifying or checking that therenter's driving score is above or meets the vehicle owner's minimumacceptable or satisfactory driving score or otherwise meets apredetermined threshold or driving score 1114.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, for each vehicle that matches the renter'sdesired characteristics and for which the renter's or driver's score isacceptable to the owner, retrieving the vehicle owner's social mediascore or profile 1116 from a memory unit.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, for each vehicle that matches the renter'sdesired characteristics and for which the renter's or driver's score isacceptable to the owner, generating a trust score based upon therenter's and the owner's social media or social media score or profiles1118.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, for each vehicle that matches the renter'sdesired characteristics and for which the renter's or driver's score isacceptable to the owner, verifying that the trust score is above thevehicle owner's minimum acceptable trust score or otherwise meets apredetermined trust threshold 1120.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission to mobile devices, for example), for each vehicle thatmatches the renter's desired characteristics and for which the renter'sor driver's score is acceptable to the owner, and for which therenter/owner trust score is above or meets a minimum acceptable trustscore, presenting those vehicles on the user interface of the platformor website for viewing available rental vehicles by the renter on theirmobile device or other computing device 1122.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), receiving or accepting arenter's selection of an individual rental vehicle 1124.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, generating a UBI (usage-based insurance) quote orUBI auto insurance quote for the rental vehicle. The UBI quote may bebased upon vehicle characteristics (such as year, make, model), therenter's driving score or profile (determined from telematics data),and/or the renter's social media score or profile.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, presenting the UBI quote to the renter viawireless communication or data transmission to their mobile device, andreceiving acceptance of, and/or electronic payment for, the UBI quoteand/or UBI for the rental period and covering/insuring the rentalvehicle 1128.

The method 1100 may include, via one or more processors, transceivers,and/or remote servers, and via wireless communication or datatransmission from a renter's mobile device, feedback on the owner andthe owner's vehicle. The feedback may be used to adjust the vehicleowner's and/or the vehicle's profile for presentation to futurepotential renters using the platform or website. The method 1100 mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

Exemplary P2P Rental Home Transaction

FIG. 12 illustrates an exemplary computer-implemented method ofconducting a P2P rental home transaction 1200. The computer-implementedmethod 1200 may be conducted by one or more local or remote processors,servers, transceivers, and/or sensors, and/or directed by computerexecutable instructions stored on non-transitory computer readable mediaor medium.

The method 1200 may include (home or other types of) renters and (homeor other types of) owners signing up to use a sharing platform, server,or website via their mobile devices or other computing devices 1202. Therenters and owners may each provide affirmative consent or permissionfor a remote server to gather or collect various types of data, such ashome or vehicle telematics data, insurance data, credit score data,social media data, social media activity data, and other types of data,including those discussed elsewhere herein. The permission to collectdata may be received, via one or more processors, transceivers, and/orremote servers, and via wireless communication or data transmission frommobile devices.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), collecting home (and/orvehicle) telematics data for each renter or prospective renter, andgenerating a risk or home score or risk or home profile for each renterbased upon the home telematics data 1204. The home telematics data mayinclude electricity usage, internet usage, water usage, presenceinformation, time-of-day information, video or image data of occupantsand pets, smart home data, home sensor data, home maintenanceinformation, home upkeep information, and other types of home telematicsdata associated with risk averse behavior of the renter.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), collecting social mediadata and/or social media activity data for each renter or prospectiverenter, and generating a social media score or profile for each renterbased upon the social media or social media activity data 1104. Thesocial media or social media activity data may include parameters orfactors discussed elsewhere herein, such as likes or dislikes, number ofonline friends, etc.

The method 1200 may include, the renter subsequently logging into theplatform or website, and via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), entering the desiredcharacteristics of a rental items, in this case, a rental home 1208. Therental home characteristics may include location, features, squarefootage, number of rooms, number of bathrooms and bedrooms, garage,location, pool, distance to night life or restaurants or beaches orother popular destinations, etc. The renter may also enter a rentalperiod and desired amount to pay 1208.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, retrieving the renter's home or homeowners scoreor profile and/or the renter's social media score or profile 1210 from amemory unit.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, identifying rental homes that have the renter'sdesired characteristics within a predetermined distance, and that areavailable during or at the rental period 1212.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, for each rental home that possesses the renter'sdesired characteristics, verifying or checking that the renter's home orhomeowners score is above or meets the home owner's minimum acceptableor satisfactory driving score or otherwise meets a predeterminedthreshold or home score 1214.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, for each home that matches the renter's desiredcharacteristics and for which the renter's home or other score isacceptable to the owner, retrieving the home owner's social media scoreor profile 1216 from a memory unit.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, for each vehicle that matches the renter'sdesired characteristics and for which the home or homeowner score isacceptable to the owner, generating a trust score based upon therenter's and the owner's social media or social media score or profiles1218.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, for each vehicle that matches the renter'sdesired characteristics and for which the renter's home or homeownerscore is acceptable to the owner, verifying that the trust score isabove the vehicle owner's minimum acceptable trust score or otherwisemeets a predetermined trust threshold 1220.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission to mobile devices, for example), for each home that matchesthe renter's desired characteristics and for which the renter's home orhomeowner score is acceptable to the owner, and for which therenter/owner trust score is above or meets a minimum acceptable trustscore, presenting those homes on the user interface of the platform orwebsite for viewing available rental homes by the renter on their mobiledevice or other computing device 1222.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers (and via wireless communication or datatransmission from mobile devices, for example), receiving or accepting arenter's selection of an individual rental home 1224.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, generating a UBI (usage-based insurance) quote orUBI homeowners insurance quote for the rental home. The UBI quote may bebased upon home characteristics (such as year, features, size, location,etc.), the renter's home or homeowners score or profile (such asdetermined from home telematics data or other data collected), and/orthe renter's social media score or profile.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, presenting the UBI quote to the renter viawireless communication or data transmission to their mobile device, andreceiving acceptance of, and/or electronic payment for, the UBI quoteand/or UBI for the rental period and covering/insuring the rental home1228.

The method 1200 may include, via one or more processors, transceivers,and/or remote servers, and via wireless communication or datatransmission from a renter's mobile device, feedback on the owner andthe owner's vehicle. The feedback may be used to adjust the vehicleowner's and/or the home's profile for presentation to future potentialrenters using the platform or website. The method 1200 may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

Exemplary UBI Embodiments

In one aspect, a computer-implemented method of determining a sharingscore between online user profiles based upon their social mediaactivity data to match online renters with rental item owners may beprovided. The method may include (1) receiving consent from an onlinerenter and multiple owners, via one or more processors, servers,sensors, and/or transceivers and via wireless communication or datatransmission over one or more radio frequency links, to capture datafrom their respective mobile devices and social media activities orwebsites; (2) collecting, via one or more processors, servers, sensors,and/or transceivers and via wireless communication or data transmissionover one or more radio frequency links, telematics data from the onlinerenter and generating a rental score or profile for the online renter;(3) collecting, via one or more processors, servers, sensors, and/ortransceivers and via wireless communication or data transmission overone or more radio frequency links, social media activity data from theonline renter's mobile device and the owner's mobile device; (4)receiving, via one or more processors, servers, sensors, and/ortransceivers and via wireless communication or data transmission overone or more radio frequency links, desired rental item characteristicsfrom the online renter's mobile device; (5) identifying, via one or moreprocessors, servers, sensors, and/or transceivers, rental itemsassociated with several owners that have the desired rental itemcharacteristics; (6) for each of the rental items identified, via one ormore processors, servers, sensors, and/or transceivers, generating asocial media score based upon the online renter's and each vehicleowner's social media activity; and/or (7) presenting, via one or moreprocessors, servers, sensors, and/or transceivers and via wirelesscommunication or data transmission over one or more radio frequencylinks, to the online renter matching rental items (the rental itemsidentified) and a social media score to on a display of the onlinerenter's mobile device.

The method may include receiving, via one or more processors, servers,sensors, and/or transceivers and via wireless communication or datatransmission over one or more radio frequency links, a selection of arental item from the online renter; generating, via one or moreprocessors, servers, sensors, and/or transceivers, a UBI quote for arental period for the rental item based upon rental item characteristicsand the online renter's telematics data; and/or transmitting, via one ormore processors, servers, sensors, and/or transceivers and via wirelesscommunication or data transmission over one or more radio frequencylinks, the UBI quote to the online renter's mobile device for onlinerenter review and/or approval.

In some embodiments, the rental item may be a vehicle, the telematicsdata may be vehicle telematics data collected by the online renter'smobile device, and the UBI quote may be a UBI auto insurance quote. Inother embodiments, the rental item may be a home, the telematics datamay be home telematics data collected by the online renter's mobiledevice or smart home controller and/or home sensors, and the UBI quotemay be a UBI homeowners insurance quote. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

In another aspect, a matching computer system for determining a sharingscore between online user profiles based upon their social mediaactivity data to facilitate P2P online transactions may be provided. Thematching computer system including at least one processor incommunication with at least one memory device, wherein the at least oneprocessor may be configured to: (1) receive consent, via an onlinerenter and multiple owners, to capture data from their respective mobiledevices and social media activities or websites; (2) collect telematicsdata from the online renter and generating a rental score or profile forthe online renter; (3) collect social media activity data from theonline renter's mobile device and the owner's mobile device; (4) receivedesired rental item characteristics from the online renter's mobiledevice; (5) identify rental items associated with several owners thathave the desired rental item characteristics; (6) for each of the rentalitems identified, generate a social media score based upon the onlinerenter's and each vehicle owner's social media activity; and/or (7)present to the online renter matching rental items (the rental itemsidentified) and a social media score to on a display of the onlinerenter's mobile device.

The at least one processor is configured to: receive a selection of arental item from the online renter; generate a UBI quote for a rentalperiod for the rental item based upon rental item characteristics andthe online renter's telematics data; and/or transmit the UBI quote tothe online renter's mobile device for online renter review and/orapproval.

In one embodiment, the rental item may be a vehicle, the telematics datamay be vehicle telematics data collected by the online renter's mobiledevice, and the UBI quote may be a UBI auto insurance quote. In anotherembodiment, the rental item may be a home, the telematics data may behome telematics data collected by the online renter's mobile device orsmart home controller and/or home sensors, and the UBI quote may be aUBI homeowners insurance quote. The computer system may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

Additional Embodiments

In one embodiment, at least one non-transitory computer-readable storagemedia having computer-executable instructions embodied thereon fordetermining a sharing score between online user profiles is provided.When executed by at least one processor, the computer-executableinstructions cause the processor to generate a first online user profileassociated with a first user. The first online user profile includesfirst insurance data, first social media data, first third party data,first user data, and/or first sharing data associated with a first user.The computer-executable instructions also cause the processor togenerate a second online user profile associated with a second user. Thesecond online user profile includes second insurance data, second socialmedia data, second third party data, second user data, and/or secondsharing data associated with a second user. The computer-executableinstructions further cause the processor to calculate a first base scoreassociated with the first user based upon the first online user profile.The first base score represents a first level of trustworthiness of thefirst user. The computer-executable instructions also cause theprocessor to calculate a second base score associated with the seconduser based upon the second online user profile. The second base scorerepresents a second level of trustworthiness of the second user. Thecomputer-executable instructions also cause the processor to determine asharing score between the first and second users based upon the firstand second base scores. The sharing score represents a level of matchingbetween the first online user profile and the second online userprofile.

An additional feature may be where the computer-executable instructionscause the processor to receive a first user-type associated with thefirst user and a second user-type associated with the second user. Thecomputer-executable instructions also cause the processor to calculatethe first base score based upon the first user-type; and calculate thesecond base score based upon the second user-type.

Another additional feature may be where the computer-executableinstructions cause the processor to generate a graphical user interface.The graphical user interface is configured to display the first basescore and the second base score, and display the sharing score betweenthe first user and the second user.

In another embodiment, at least one non-transitory computer-readablestorage media having computer-executable instructions embodied thereonfor determining a sharing score between online user profiles isprovided. When executed by at least one processor, thecomputer-executable instructions cause the processor to receive auser-listing associated with a first user. The user-listing includes aplurality of items offered for sale, rent, and/or lease. The first userof the user-listing may be associated with a first user-typecorresponding to an owner. The computer-executable instructions alsocause the processor to calculate an item base score for each of theplurality of items. Each item base score may be calculated based upon afirst online user profile associated with the first user. Thecomputer-executable instructions also further the processor to calculatea second user base score based upon a second online user profileassociated with a second user. The second user may be associated with asecond user-type corresponding to a renter. The computer-executableinstructions also cause the processor to determine a sharing scorebetween each of the plurality of items and the second user. Each sharingscore may be based upon each respective item base score and the seconduser base score, and each sharing score represents a level of matchingbetween each item and the second online user profile.

An additional feature may be where the user-listing includes listingavailability dates and listing rates associated with the listingavailability dates.

Another additional feature may be where the computer-executableinstructions cause the processor to generate a graphical user interface.The graphical user interface is configured to display the user-listingassociated with the first user. The graphical user interface is alsoconfigured to receive a first selection input from the second user. Thefirst selection input indicates that at least one selected item from theuser-listing. The graphical user interface is further configured todisplay, in response to receiving the first selection input, the sharingscore between the at least one selected item and the second user.

An additional feature may be where the computer-executable instructionscause the processor to generate a graphical user interface that isconfigured to display, in response to receiving the first selectioninput, at least one listing availability date and the associated listingrate. The graphical user interface is also configured to receive asecond selection input from the second user. The second selection inputindicates an available date. The graphical user interface is furtherconfigured to concurrently display a location on a map of the at leastone selected item and details associated with the at least one selecteditem.

Another additional feature may be where the computer-executableinstructions cause the processor to generate a graphical user interfaceis configured to display the user-listing and each determined sharingscore between each of the plurality of items and the second user. Theplurality of items may include at least one of goods and services.

In another embodiment, a matching computer system for determining atrust score for a user based upon at least social media data andinsurance data is provided. The matching computer system includes atleast one processor in communication with at least one memory device.The at least one processor is configured to register, with the matchingcomputer system, one or more users. The at least one processor is alsoconfigured to receive consent from one or more users to capture socialmedia data associated with social media activities of each respectiveuser. The at least one processor is further configured to collect thesocial media data and insurance data from each registered user, andretrieve the social media data and the insurance data associated witheach registered user. The at least one processor is also configured todetermine a trust score for each registered user based, at least inpart, upon each respective social media data and each respectiveinsurance data, wherein the trust score represents a level oftrustworthiness of the user.

An additional feature may be where the at least one processor isconfigured to collect insurance data including telematics data from eachregistered user.

Another additional feature may be where each registered user may be oneof a consumer and a provider. The consumer may be associated with aconsumer computing device, and the provider may be associated with aprovider computing device.

An additional feature may be where the trust score may be one of aconsumer trust score associated with the consumer and a provider trustscore associated with the provider.

Another additional feature may be where the at least one processor isconfigured to compare the provider trust score to one or more consumerthresholds. Each consumer threshold may be associated with eachconsumer; determine, based upon the comparison, one or more consumershaving the respective consumer threshold being satisfied by the providertrust score; and cause to display, on the provider computing device, theone or more one consumers.

An additional feature may be where the at least one processor isconfigured to compare the consumer trust score to one or more providerthresholds. Each provider threshold may be associated with eachprovider. The at least one processor is also configured to determine,based upon the comparison, one or more providers having the respectiveprovider threshold being satisfied by the consumer trust score. The atleast one processor is further configured to cause to display, on theconsumer computing device, one or more items offered by the determinedone or more providers.

Another additional feature may be where the at least one processor isconfigured to receive a selection of a one of the one or more itemsoffered by the determined one or more providers. The at least oneprocessor is also configured to generate a user-based insurance (UBI)quote for a rental period for the selected item based upon itemcharacteristics of the selected item and telematics data collected fromthe consumer. The at least one processor is further configured totransmit the UBI quote to the consumer computing device, and cause todisplay, the consumer computing device, the UBI quote for at least oneof review and approval by the consumer.

An additional feature may be where the selected item is a vehicle, thetelematics data is vehicle telematics data collected via the consumercomputing device, and the UBI quote is a UBI auto insurance quote.

Another additional feature may be where the selected item is a home, thetelematics data is home telematics data collected via at least one ofthe consumer computing device, a smart home controller, and homesensors, and the UBI quote is a UBI homeowners insurance quote.

An additional feature may be where the at least one processor isconfigured to determine a social media score for each registered userbased upon each respective social media data. The at least one is alsoconfigured to determine the trust score for each registered user based,at least in part, upon each respective social media score and eachrespective insurance data.

In an additional embodiment, a computer-implemented method ofdetermining a trust score for a user based upon at least social mediadata and insurance data is provided. The method may be implemented by amatching computer system. The method includes registering, with thematching computer system via the one or more processors, servers,sensors, and/or transceivers, one or more users. The method alsoincludes receiving, via the one or more processors, servers, sensors,and/or transceivers and via wireless communication or data transmissionover one or more radio frequency links, consent from one or more usersto capture social media data associated with social media activities ofeach respective user. The method further includes collecting, via theone or more processors, servers, sensors, and/or transceivers and viathe wireless communication or data transmission over one or more radiofrequency links, the social media data and insurance data from eachregistered user. The method also includes retrieving, via the one ormore processors, servers, sensors, and/or transceivers, social mediadata and insurance data associated with each registered user. The methodfurther includes determining, via the one or more processors, servers,sensors, and/or transceivers, a trust score for each registered userbased, at least in part, upon each respective social media data and eachrespective insurance data. The trust score represents a level oftrustworthiness of the user.

Another additional feature may be where the method includes collecting,via the one or more processors, servers, sensors, and/or transceiversand via the wireless communication or data transmission over one or moreradio frequency links, telematics data.

An additional feature may be where each registered user is one of aconsumer and a provider. The consumer may be associated with a consumercomputing device, and the provider may be associated with a providercomputing device.

Another additional feature may be where the trust score is one of aconsumer trust score associated with the consumer and a provider trustscore associated with the provider.

An additional feature may be where the method includes comparing, viathe one or more processors, servers, sensors, and/or transceivers, theprovider trust score to one or more consumer thresholds, each consumerthreshold associated with each consumer. The method also includesdetermining, based upon the comparison, via the one or more processors,servers, sensors, and/or transceivers, one or more consumers having therespective consumer threshold being satisfied by the provider trustscore. The method further includes causing to display on the providercomputing device, via the one or more processors, servers, sensors,and/or transceivers and via the wireless communication or datatransmission over one or more radio frequency links, the one or more oneconsumers.

Another additional feature may be where the method includes comparing,via the one or more processors, servers, sensors, and/or transceivers,the consumer trust score to one or more provider thresholds. Eachprovider threshold may be associated with each provider. The method alsoincludes determining, based upon the comparison via the one or moreprocessors, servers, sensors, and/or transceivers, one or more providershaving the respective provider threshold being satisfied by the consumertrust score. The method further includes causing to display on theconsumer computing device, via the one or more processors, servers,sensors, and/or transceivers and via the wireless communication or datatransmission over one or more radio frequency links, one or more itemsoffered by the determined one or more providers.

An additional feature may be where the method includes receiving, viathe one or more processors, servers, sensors, and/or transceivers andvia the wireless communication or data transmission over one or moreradio frequency links, a selection of one of the one or more itemsoffered by the determined one or more providers. The method alsoincludes generating, via the one or more processors, servers, sensors,and/or transceivers, a user-based insurance (UBI) quote for a rentalperiod for the selected item based upon item characteristics of theselected item and telematics data collected from the consumer. Themethod further includes transmitting, via the one or more processors,servers, sensors, and/or transceivers and via the wireless communicationor data transmission over one or more radio frequency links, the UBIquote to the consumer computing device. The method also includes causingto display on the consumer computing device, via the one or moreprocessors, servers, sensors, and/or transceivers and via the wirelesscommunication or data transmission over one or more radio frequencylinks, the UBI quote for at least one of review and approval by theconsumer.

An additional feature may be where the selected item is a vehicle, thetelematics data is vehicle telematics data collected via the consumercomputing device, and the UBI quote is a UBI auto insurance quote.

An additional feature may be where the selected item is a home, thetelematics data is home telematics data collected via at least one ofthe consumer computing device, a smart home controller, and homesensors, and the UBI quote is a UBI homeowners insurance quote.

An additional feature may be where the method includes determining, viathe one or more processors, servers, sensors, and/or transceivers, asocial media score for each registered user based upon each respectivesocial media data. The method also includes determining, via the one ormore processors, servers, sensors, and/or transceivers, the trust scorefor each registered user based, at least in part, upon each respectivesocial media score and each respective insurance data.

In an additional embodiment, at least one non-transitorycomputer-readable storage media having computer-executable instructionsembodied thereon for determining a trust score for a user based upon atleast social media data and insurance data is provided. When executed byat least one processor included in a matching computer system, thecomputer-executable instructions cause the at least one processor toregister, with the matching computer system, one or more users, andreceive consent from one or more users to capture social media dataassociated with social media activities of each respective user. Thecomputer-executable instructions also cause the at least one processorto collect the social media data and insurance data from each registereduser, and retrieve the social media data and the insurance dataassociated with each registered user. The computer-executableinstructions further cause the at least one processor to determine atrust score for each registered user based, at least in part, upon eachrespective social media data and each respective insurance data. Thetrust score represents a level of trustworthiness of the user.

An additional feature may be where the computer-executable instructionscause the at least one processor to collect insurance data includingtelematics data from each registered user.

Another additional feature may be where each registered user is one of aconsumer and a provider. The consumer may be associated with a consumercomputing device, and the provider may be associated with a providercomputing device.

An additional feature may be where the trust score is one of a consumertrust score associated with the consumer and a provider trust scoreassociated with the provider.

Another additional feature may be where the computer-executableinstructions cause the at least one processor to compare the providertrust score to one or more consumer thresholds. Each consumer thresholdmay be associated with each consumer. The computer-executableinstructions also cause the at least one processor to determine, basedupon the comparison, one or more consumers having the respectiveconsumer threshold being satisfied by the provider trust score. Thecomputer-executable instructions further cause the at least oneprocessor to cause to display, on the provider computing device, the oneor more one consumers.

An additional feature may be where the computer-executable instructionscause the at least one processor to compare the consumer trust score toone or more provider thresholds. Each provider threshold associated witheach provider. The computer-executable instructions also cause the atleast one processor to determine, based upon the comparison, one or moreproviders having the respective provider threshold being satisfied bythe consumer trust score. The computer-executable instructions furthercause the at least one processor to cause to display, on the consumercomputing device, one or more items offered by the determined one ormore providers.

Another additional feature may be where the computer-executableinstructions cause the at least one processor to receive a selection ofa one of the one or more items offered by the determined one or moreproviders. The computer-executable instructions also cause the at leastone processor to generate a user-based insurance (UBI) quote for arental period for the selected item based upon item characteristics ofthe selected item and telematics data collected from the consumer. Thecomputer-executable instructions further cause the at least oneprocessor to transmit the UBI quote to the consumer computing device,and cause to display, on the consumer computing device, the UBI quotefor at least one of review and approval by the consumer.

An additional feature may be where the selected item is a vehicle, thetelematics data is vehicle telematics data collected via the consumercomputing device, and the UBI quote is a UBI auto insurance quote.

Another additional feature may be where the selected item is a home, thetelematics data is home telematics data collected via at least one ofthe consumer computing device, a smart home controller, and homesensors, and the UBI quote is a UBI homeowners insurance quote.

An additional feature may be where the computer-executable instructionscause the at least one processor to determine a social media score foreach registered user based upon each respective social media data. Thecomputer-executable instructions also cause the at least one processorto determine the trust score for each registered user based, at least inpart, upon each respective social media score and each respectiveinsurance data.

ADDITIONAL CONSIDERATIONS

With the foregoing, an insurance customer may opt-in to a socialinsurance group or other type of program. After the insurance customerprovides their affirmative consent, an insurance provider remote servermay collect data from the member's mobile device, user computing device,or, if the asset includes a computing device (e.g., a smart vehicle,autonomous or semi-autonomous vehicle, smart home controller, or othersmart devices)—such as with the customer's permission or affirmativeconsent. The data collected may be related to social insurance groupactivity and/or individual member activity for insured assets before(and/or after) an insurance-related event, including those eventsdiscussed elsewhere herein.

As will be appreciated based upon the foregoing specification, theabove-described embodiments of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof. Anysuch resulting program, having computer-readable code means, may beembodied or provided within one or more computer-readable media, therebymaking a computer program product, i.e., an article of manufacture,according to the discussed embodiments of the disclosure. Thecomputer-readable media may be, for example, but is not limited to, afixed (hard) drive, diskette, optical disk, magnetic tape, semiconductormemory such as read-only memory (ROM), and/or any transmitting/receivingmedium such as the Internet or other communication network or link. Thearticle of manufacture containing the computer code may be made and/orused by executing the code directly from one medium, by copying the codefrom one medium to another medium, or by transmitting the code over anetwork.

These computer programs (also known as programs, software, softwareapplications, “apps”, or code) include machine instructions for aprogrammable processor, and can be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” “computer-readable medium” refers to any computer programproduct, apparatus and/or device (e.g., magnetic discs, optical disks,memory, Programmable Logic Devices (PLDs)) used to provide machineinstructions and/or data to a programmable processor, including amachine-readable medium that receives machine instructions as amachine-readable signal. The “machine-readable medium” and“computer-readable medium,” however, do not include transitory signals.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an exemplary embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Wash.). In yet another embodiment,the system is run on a mainframe environment and a UNIX® serverenvironment (UNIX is a registered trademark of X/Open Company Limitedlocated in Reading, Berkshire, United Kingdom). The application isflexible and designed to run in various different environments withoutcompromising any major functionality. In some embodiments, the systemincludes multiple components distributed among a plurality of computingdevices. One or more components may be in the form ofcomputer-executable instructions embodied in a computer-readable medium.The systems and processes are not limited to the specific embodimentsdescribed herein. In addition, components of each system and eachprocess can be practiced independent and separate from other componentsand processes described herein. Each component and process can also beused in combination with other assembly packages and processes.

In some embodiments, registration of users for the social insurancegroup includes opt-in informed consent of users to data usage by theinteractive 3D image projection system consistent with consumerprotection laws and privacy regulations. In some embodiments, theenrollment data and/or other collected data may be anonymized and/oraggregated prior to receipt such that no personally identifiableinformation (PII) is received. In other embodiments, the system may beconfigured to receive application data and/or other collected data thatis not yet anonymized and/or aggregated, and thus may be configured toanonymize and aggregate the data. In such embodiments, any PII receivedby the system is received and processed in an encrypted format, or isreceived with the consent of the individual with which the PII isassociated. In situations in which the systems discussed herein collectpersonal information about individuals, or may make use of such personalinformation, the individuals may be provided with an opportunity tocontrol whether such information is collected or to control whetherand/or how such information is used. In addition, certain data may beprocessed in one or more ways before it is stored or used, so thatpersonally identifiable information is removed.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “exemplary embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

The patent claims at the end of this document are not intended to beconstrued under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being expressly recited in the claim(s).

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the disclosure, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

We claim:
 1. A matching computer system for determining a sharing scorebetween online user profiles, the matching computer system including atleast one processor in communication with at least one memory device,wherein the at least one processor is configured to: generate a firstonline user profile associated with a first user, wherein the firstonline user profile comprises first social media data of the first userand at least one of first insurance data, first third party data, firstuser data, and first sharing data associated with the first user;generate a second online user profile associated with a second user,wherein the second online user profile comprises second social mediadata of the second user and at least one of second insurance data,second third party data, second user data, and second sharing dataassociated with the second user; calculate a first base score associatedwith the first user based upon the first online user profile, whereinthe first base score represents a first level of trustworthiness of thefirst user, and wherein the first level of trustworthiness is associatedwith a first driving history of the first user, a first level ofmaintenance of a first vehicle of the first user, and first social mediaactivity of the first user; calculate a second base score associatedwith the second user based upon the second online user profile, whereinthe second base score represents a second level of trustworthiness ofthe second user, and wherein the second level of trustworthiness isassociated with a second driving history of the second user, a secondlevel of maintenance of a second vehicle of the second user, and secondsocial media activity of the second user; compare (i) the first basescore to the second base score, and (ii) the first social media data inthe first online user profile to the second social media data in thesecond online user profile and identifying similarities therein; anddetermine the sharing score between the first and second users basedupon the comparison, wherein the sharing score is a unique score betweenthe first and second users and represents a level of matching betweenthe first online user profile and the second online user profile.
 2. Thematching computer system of claim 1, wherein the at least one processoris further configured to: receive a first user-type associated with thefirst user and a second user-type associated with the second user;calculate the first base score based upon the first user-type; andcalculate the second base score based upon the second user-type.
 3. Thematching computer system of claim 1, wherein the at least one processoris further configured to: generate a graphical user interface, whereinthe graphical user interface is configured to: display the first basescore and the second base score; and display the sharing score betweenthe first user and the second user.
 4. The matching computer system ofclaim 1, wherein the first online user profile further comprises firsttelematics data associated with the first vehicle of the first user, andwherein the second online user profile comprises second telematics dataassociated with a second vehicle of the second user.
 5. A matchingcomputer system for determining a sharing score between online userprofiles, the matching computer system including at least one processorin communication with at least one memory device, wherein the at leastone processor is configured to: receive a user-listing associated with afirst user, wherein the user-listing includes a plurality of itemsoffered for at least sale, rent, and lease, and wherein the first useris associated with a first user-type corresponding to an owner;calculate an item base score for each of the plurality of items, whereineach item base score is calculated based upon a first online userprofile associated with the first user, wherein the first online userprofile comprises first social media data of the first user and a firstuser base score representing a first level of trustworthiness of thefirst user, and wherein the first level of trustworthiness is associatedwith a first driving history of the first user, a first level ofmaintenance of a first vehicle of the first user, and first social mediaactivity of the first user; calculate a second user base score basedupon a second online user profile associated with a second user, whereinthe second user is associated with a second user-type corresponding to arenter, the second online user profile comprising second social mediadata of the second user and the second user base score representing asecond level of trustworthiness of the second user, and wherein thesecond level of trustworthiness is associated with a second drivinghistory of the second user, a second level of maintenance of a secondvehicle of the second user, and second social media activity of thesecond user; and determine an item sharing score between each of theplurality of items and the second user, wherein each item sharing scoreis determined by comparing (i) each respective item base score to thesecond user base score, and (ii) the first social media data in thefirst online user profile to the second social media data in the secondonline user profile and identifying similarities therein, and whereineach item sharing score is a unique score between the second user andeach of the plurality of items and represents a level of matchingbetween each item and the second online user profile.
 6. The matchingcomputer system of claim 5, wherein the user-listing includes listingavailability dates and listing rates associated with the listingavailability dates.
 7. The matching computer system of claim 6, whereinthe at least one processor is further configured to: generate agraphical user interface, wherein the graphical user interface isconfigured to: display the user-listing associated with the first user;receive a first selection input from the second user, the firstselection input indicating at least one selected item from theuser-listing; and display, in response to receiving the first selectioninput, the item sharing score between the at least one selected item andthe second user.
 8. The matching computer system of claim 7, wherein theat least one processor is further configured to: generate a graphicaluser interface, wherein the graphical user interface is configured to:display, in response to receiving the first selection input, at leastone listing availability date and the associated listing rate; receive asecond selection input from the second user, the second selection inputindicating an available date; and concurrently display a location on amap of the at least one selected item and details associated with the atleast one selected item.
 9. The matching computer system of claim 5,wherein the at least one processor is further configured to: generate agraphical user interface, wherein the graphical user interface isconfigured to display the user-listing and each determined item sharingscore between each of the plurality of items and the second user. 10.The matching computer system of claim 5, wherein the plurality of itemsincludes at least one of goods and services.
 11. The matching computersystem of claim 5, wherein the first online user profile furthercomprises first telematics data associated with the first vehicle of thefirst user, and wherein the second online user profile comprises secondtelematics data associated with a second vehicle of the second user. 12.A computer-implemented method for determining a sharing score betweenonline user profiles, the method implemented by a matching computersystem including at least one processor, the method comprising:generating a first online user profile associated with a first user,wherein the first online user profile comprises first social media dataof the first user at least one of first insurance data, first thirdparty data, first user data, and first sharing data associated with thefirst user; generating a second online user profile associated with asecond user, wherein the second online user profile comprises secondsocial media data of the second user and at least one of secondinsurance data, second third party data, second user data, and secondsharing data associated with the second user; calculating a first basescore associated with the first user based upon the first online userprofile, wherein the first base score represents a first level oftrustworthiness of the first user, and wherein the first level oftrustworthiness is associated with a first driving history of the firstuser, a first level of maintenance of a first vehicle of the first user,and first social media activity of the first user; calculating a secondbase score associated with the second user based upon the second onlineuser profile, wherein the second base score represents a second level oftrustworthiness of the second user, and wherein the second level oftrustworthiness is associated with a second driving history of thesecond user, a second level of maintenance of a second vehicle of thesecond user, and second social media activity of the second user;comparing (i) the first base score to the second base score, and (ii)the first social media data in the first online user profile to thesecond social media data in the second online user profile andidentifying similarities therein; and determining the sharing scorebetween the first and second users based upon the comparison, whereinthe sharing score is a unique score between the first and second usersand represents a level of matching between the first online user profileand the second online user profile.
 13. The computer-implemented methodof claim 12, wherein the method further comprises: receiving a firstuser-type associated with the first user and a second user-typeassociated with the second user; calculating the first base score basedupon the first user-type; and calculating the second base score basedupon the second user-type.
 14. The computer-implemented method of claim12, wherein the method further comprises: generating a graphical userinterface, wherein the graphical user interface is configured to:display the first base score and the second base score; and display thesharing score between the first user and the second user.
 15. Acomputer-implemented method for determining a sharing score betweenonline user profiles, the method implemented by a matching computersystem including at least one processor, the method comprising:receiving a user-listing associated with a first user, wherein theuser-listing includes a plurality of items offered for at least sale,rent, and lease, and wherein the first user is associated with a firstuser-type corresponding to an owner; calculating an item base score foreach of the plurality of items, wherein each item base score iscalculated based upon a first online user profile associated with thefirst user, wherein the first online user profile comprises first socialmedia data of the first user and a first user base score representing afirst level of trustworthiness of the first user, and wherein the firstlevel of trustworthiness is associated with a first driving history ofthe first user, a first level of maintenance of a first vehicle of thefirst user, and first social media activity of the first user;calculating a second user base score based upon a second online userprofile associated with a second user, wherein the second user isassociated with a second user-type corresponding to a renter, the secondonline user profile comprising second social media data of the seconduser and the second user base score representing a second level oftrustworthiness of the second user, and wherein the second level oftrustworthiness is associated with a second driving history of thesecond user, a second level of maintenance of a second vehicle of thesecond user, and second social media activity of the second user; anddetermining an item sharing score between each of the plurality of itemsand the second user, wherein each item sharing score is determined bycomparing (i) each respective item base score to the second user basescore, and (ii) the first social media data in the first online userprofile to the second social media data in the second online userprofile and identifying similarities therein, and wherein each itemsharing score is a unique score between the second user and each of theplurality of items and represents a level of matching between each itemand the second online user profile.
 16. The computer-implemented methodof claim 15, wherein the user-listing includes listing availabilitydates and listing rates associated with the listing availability dates.17. The computer-implemented method of claim 16, wherein the methodfurther comprises: generating a graphical user interface, wherein thegraphical user interface is configured to: display the user-listingassociated with the first user; receive a first selection input from thesecond user, the first selection input indicating at least one selecteditem from the user-listing; and display, in response to receiving thefirst selection input, the item sharing score between the at least oneselected item and the second user.
 18. The computer-implemented methodof claim 17, wherein the method further comprises: generating agraphical user interface, wherein the graphical user interface isconfigured to: display, in response to receiving the first selectioninput, at least one listing availability date and the associated listingrate; receive a second selection input from the second user, the secondselection input indicating an available date; and concurrently display alocation on a map of the at least one selected item and detailsassociated with the at least one selected item.
 19. Thecomputer-implemented method of claim 15, wherein the method furthercomprises: generating a graphical user interface, wherein the graphicaluser interface is configured to display the user-listing and eachdetermined item sharing score between each of the plurality of items andthe second user.
 20. The computer-implemented method of claim 15,wherein the plurality of items includes at least one of goods andservices.